TW200417167A - Communication system and method therefor - Google Patents

Abstract

A communication system (10) having an echo canceller (20) is disclosed. One embodiment of the echo canceller includes an adaptive filter (28) used to provide an estimate of reflected echo which is removed from the send signal. The echo canceller may also include a near-end talker signal detector (26) which may be used to prevent the adaptive filter from adapting when a near-end talker signal is present. The echo canceller may also include a nonlinear processor (32) used to further reduce any residual echo and to preserve background noise. The echo canceller may also include a monitor and control unit (30) which may be used to monitor the filter coefficients and gain of the adaptive filter to maintain stability of the echo canceller, estimate pure delay, detect a tone, and inject a training signal (41). The echo canceller may also include a nonadaptive filter (31) used to reduce the length of the adaptive filter.

Description

200417167 (1) Description of the invention: [Technical field to which the invention belongs] The present invention generally relates to audio indicators', and more particularly to methods and devices for audio indicators in a communication system. [Prior art] Echo cancellation is used in telecommunication networks (such as the Public Switching Telephone Network (PSTN) or Packet Telephony (PT) networks), by eliminating or reducing the electronic or Linear echo ensures speech quality. This electronic or linear echo can originate from the impedance mismatch of the merging circuit (a device used to convert signals from the four-wire communication network interface to a two-wire local user loop, and vice versa), and noticeably long delays in the communication network Disturbances that can be noticeable or even unacceptable can occur between telephone voice communications. Therefore, there is a need for an echo canceller capable of completely eliminating or reducing echo in a communication network to an acceptable level. Similarly, there is a need for an echo canceller capable of detecting audio received through a communication network while maintaining stability. [Summary of the Invention] This application has been filed in U.S. Patent Application Serial Nos. 10 / 178,176, 10 / 178,154, 10 / 178,427, 10 / 178,560, and 10 / 178,597, filed on June 24, 2002. This is a US patent application filed with lawyer file number SCI 2026TS, titled "Monitoring and Control of Adaptive Filters in Communication Systems", and a filed US patent application filed with lawyer file number SC11977TS is titled "Audio Signal Non Method and Apparatus for Linear Processing ", US patent application filed with lawyer file number SC12120TS at the same time, entitled" Communication 86017 200417167 Method and Device for Pure Delay Estimation in the System ", and US Patent Application Filed with Lawyer Case Number SC12107TS at the same time , Titled "Method and Apparatus for Performing Adaptive Filtering", which are all transferred to the assignee of the present invention. [Embodiment] As used herein, the term "bus" is used to indicate a plurality of signals or conductors' which can be used to transmit one or more various types of information, such as data, bit addresses, controls, or states. The conductors discussed herein can be explained or explained with reference to a single conductor, a plurality of conductors, a unidirectional conductor, or a bidirectional conductor. However, different embodiments may vary the implementation of the conductor. For example, separate unidirectional conductors can be used instead of bidirectional conductors and vice versa. Similarly, a plurality of conductors may be replaced with a single conductor that transmits multiple signals continuously or in a time division multiplexed manner. Similarly, a single conductor carrying multiple signals can be divided into a variety of different conductors carrying a subset of these signals. Therefore, there are many options for transmitting signals. The terms "determination" and "elimination" (or "negation") are used to indicate signals, status bits, or similar devices that are in a logically correct or logically incorrect state, respectively. If the logical bit level A and -avoid U7L of the logically correct state are-, the logical error state < logical bit level is zero. If the logical bit of the logically correct state is zero, 9 * 1 ίΜill% A! 5, the logical bit of the logically incorrect state is ° sign factory * = operation operation, or other types: storage; = only for the present invention Delay used in the document. Also, it should be noted that the iA and Yanggang schemes of the phases described in this paper usually use the same y. Although the same variable schema can be reused by the m®? Boat to represent different devices in different groups of related units. For example, with regard to the special I 疋, T, and, and the pattern, M can represent the measurement cycle ', and with respect to different sets of patterns, Fe M can be used as a counter value. However, for each variable name used, descriptions are provided in the formulas and drawings below 86017 -7- 200417167. Connectivity FIG. 1 illustrates a specific embodiment of a communication system 10. The communication system 10 includes a transmitter / receiver 12, an interface 13, a merging circuit 16 (also referred to as a merging 16), an echo canceller 20, a communication network 24, an echo canceller 22, an interface 15, an merge 18, and Transmitter / receiver 14. The interface 13 includes a merge 16 and the interface 15 includes a parallel port 8. The transmitter / receiver 12 is bidirectionally coupled to the merging i6 (wherein in a specific embodiment, the transponder / receiver 12 is coupled to the merging 16 by a two-wire connection, such as a twisted pair). The coupling 16 is coupled to the echo canceller 20, and the transmission signal Sin 37 is provided to the echo canceller 20 through a unidirectional body, and the reception signal Rout 4 is received from the echo cancellation benefit 20 through a unidirectional body. In the embodiment, each of Sin 37 and Rout 40 is provided and received through two lines). The echo canceller 20 is coupled to the communication network 24 and provides an echo cancellation transmission signal sout 42 to the communication network 24 and receives a Rin 43 from the communication network 24. Similarly, the transmitter / receiver 14 is bidirectionally coupled to the merging 18 (wherein in a specific embodiment, the transmitter / receiver 14 is coupled to the merging 18 by a two-wire connection, such as a twisted pair '). The union 18 is coupled to the echo canceller 22, and provides a signal to the echo canceller 22 through a unidirectional conductor, and receives a signal from the echo canceller 22 through a unidirectional conductor (in a specific embodiment, each group of unidirectional conductors May be twisted pair). The echo canceller 22 is coupled to the communication network 24 and provides echo cancellation to transmit signals to and receive signals from the communication network 24. The control 17 may be a control bus including one or more control signals, which may be provided to each transmitter / receiver 12, merge 16, echo canceller 20, communication network 24, echo canceller 22, Union 18 and transmitter / receiver 86017 200417167 device 14. Therefore, in a specific embodiment, the control i7 is coupled to each unit in the communication system 10, while in an alternative embodiment, only a part of the units needs to communicate with the control 17. FIG. 2 is a specific embodiment of a worker of the echo canceller 20 of FIG. 1. It should be noted that in the specific embodiment described with reference to Fig. 2, the echo canceller 2 () is called a near-end echo canceller and the echo canceller 22 is called a far-end echo canceller. However, it should be understood that in the case where the echo canceller 22 is at the near end of the communication system 10 and the echo canceller is at the far end, the echo canceller illustrated in FIG. 2 may also be referred to as the echo canceller 22. The echo canceller 20 includes a DC notch filter 45, a selective non-adaptive filter 3, an adder 34, a selective non-adaptive filter 35, a gain control 33, a non-linear processor 32, and a near-end signal detection. Tester%, adaptive filter 28, monitoring and control unit 30, DC notch wave filter 49 and adder 36. The DC notch wave filter 45 receives Sin 37 and outputs Sin 38 to the near-end signal detector 26 and the monitoring and control unit 30. If there is a non-adaptive filter 31, Sin 38 is also provided to the non-adaptive filter 31, which is coupled to receive control from the monitoring and control unit 30 and output sin 39 to the adder 34. However, 'Square non-adaptive filter 3 1', then S in 3 8 is the same as Sin 39 of input addition cry 34. The adder 34 receives the Sin 39 and the echo estimation signal 48 from the adaptive filter 28, and provides an error signal 46 to the gain control 33, the near-end signal detector 26, and the monitoring and control unit 30. The gain control 33 is bidirectionally coupled to the monitoring and control unit 30 and is coupled to provide an error signal 47 to the non-linear processor 32. If there is a non-adaptive filter 35 in the echo canceller 20, in a specific embodiment, the gain control 33 is in the non-adaptive filter 35 that also receives the error signal 46, and is bidirectionally coupled to the monitoring and control unit 30 and Provide 86017 200417167 error signal 47 1 linear processor 32 dual-to-monitor and control unit 30 and provide Sout 42. The monitoring and control unit 30 is also coupled to the control 17, receives Rin 43, provides a serial signal 41 to the addition ㈣, receives Rm 44 'from the dc notch wave filter 49 and combines it bidirectionally to the adaptability> wave filter 28 and Near-end signal detection state 26. The DC notch wave filter 49 receives the output of the adder% ($ 0 plus 40) and provides Rin 44 to the near-end signal detector 26, the adaptive filter "and the monitoring and control unit 30. The adder 36 receives the serial signal 41 & Rin 43 and provide Rout 40. Figure 3 is a specific embodiment of the near-end signal detector 26 of Figure 2. The near-end signal detector 26 includes a near-end signal level estimator 50, and a far end. Signal level estimator 52, Sin bluff level estimator 54, background processor 56, near-end signal detection limit selector 58 and near-end signal detector 60. Near-end signal level estimator 50 receives The error signal 46 is also coupled to the near-end signal detector 60. The far ^ k level estimator is adapted to receive Rin 44 and is also coupled to the near-end signal detection limit selection benefit 58. Sin signal level estimation The device 54 is integrated to receive Sin 38 and is also coupled to the near-end signal detector 60. The background processor 56 is coupled to the monitoring and control unit 30, the near-end signal detection limit selector 58 and the near-end signal detection器 60。 Near-end signal detector 60 is also coupled to the near-end signal detection limit selector 58 and monitoring and control Element 30. Fig. 4 is a specific embodiment of the adaptive filter 28 of Fig. 2. The adaptive filter 28 includes an adaptive filter 62, a selective non-adaptive filter 64, and a selective delay 66. Assume non-adaptive The adaptive filter 64 and the delay 66 both appear in the adaptive wave filter 28. The delay 66 receives Rin 44 and is coupled to the non-adaptive filter 64 and the monitoring and control unit 30. The non-adaptive filter 64 is coupled to 86017 -10 -200417167 Delay 66, adaptive filter 62 and monitoring and control unit 30. Adaptive filter 62 is coupled to receive error signal 46 and coupled to provide echo estimation signal 48, and is also coupled to monitoring and control unit 30. If there is no non-adaptive filter-wave 64, the delay 66 is directly coupled to the adaptive filter 62. If there is no delay 66, the non-adaptive filter 64 receives Rin44. If neither the delay 66 nor the non-adaptive filter 64, The adaptive filter 62 receives Rin44. Fig. 5 is a specific embodiment of the non-linear processor 32 of Fig. 2. The non-linear processor 32 includes a signal level estimator, a non-linear processor controller 74, and an adaptive background. Level estimator 96 It is bidirectionally coupled to the monitoring and control unit 30. The signal level estimator 68 includes a near-end signal level estimator 70 and a far-end signal level estimator 72. The non-linear processor controller 74 includes a non-linear processor activation control Generator 76, non-linear processor shutdown controller 78, suitable noise generator 86, noise level matcher 82, and output signal mixer 84. The adaptive horizon level estimator 96 includes a short-term background level estimator 88. Background level estimator controller 90, long-term background level estimator 92, and background level adapter 94. The near-end signal level estimator 70 receives the error signal 47 and is coupled to the non-linear processor activation controller 76 and the background level estimator controller 90. The T-level estimator 72 at the transport end receives Rin 44 and is coupled to the non-linear processor enable controller 76, the non-linear processor shutdown controller 78, and the background level estimator controller 90. The non-linear processor starts the controller% and the non-linear processor turns off the controller 7_ to the appropriate noise generator 86, and to the noise level matcher 82. The output signal mixer 84 is coupled to the noise level matching w 82 to receive the error number 47 and provides §0 plus 42. The short-term background level estimator 88 is coupled to the background level adapter 94 and receives the error signal 47. Background level 86017 11-200417167 Quasi-estimator controller_ Combined with short-term background level and long-term background level estimator 92. Long-term background level estimation receives error signal 47 and is coupled to background level adapter 94, It is coupled to the noise level matcher 82. FIG. 6 is a specific embodiment of a part of the monitoring and control unit 30, which includes a gain monitor 100 and a filter coefficient monitor 102. The gain monitor 1 〇〇 Receives Sin 38, error signal 46, and is coupled to adaptive filter 28 and add-on control 33. Filter coefficient monitor 102 is coupled to adaptive filter 28. Figure 7 is another part of monitoring and control unit 30 A part of a specific embodiment includes a decimal filter 104 and 108, a ten-to-one sampler 106 and 110, a near-end signal detector 114, a selective comparator 112, and a strong echo return loss (Echo Return Loss Enhancement; ERLE) Device 116, power estimators 120 and 118, adaptive filter system 128, and noise generator 132. Adaptive filter system 128 includes adaptive filter 122, maximum positioning reference 124, and delay determination 126. Decimal filter 1 〇4 receives Rin 44 and closes it to ten out of one sampler 106. Decimal filter ι08 receives Sin 38 and unbinds it to ten out of one sampler 110. Ten out of one sampler 106 is coupled to the near-end signal price detector 114. Power estimator 120 and adaptive filter 122. Power estimator 120 and near-end signal detector 114 are coupled to adaptive filter system 128. Selective comparator 112, if present in monitoring and control unit 30, receives The error signal 46 and Sin 38 are coupled to the adaptive filter system 128. One of the ten samplers 110 is coupled to the power estimator U8 and the adaptive filter 122. The power estimator 118 is coupled to the ERLE estimator 116 and the adaptive filter 2817 and 86017 -12-200417167 adaptive filter 122 is coupled to the near-end signal detector 114, the ERLE estimator 116 and the maximum positioner 124. The maximum positioner 124 is coupled to the delay determination 126, which provides The estimated delay is 130 to the adaptive filter 28. The noise generator 132 receives Rin 43 and is coupled to provide the injected signal 41 to the adder 36. The portion of the monitoring and control unit 30 of Fig. 7 is also combined to control 17. Fig. 8 series A specific embodiment of another part of the monitoring and control unit 30 includes a storage 150, a power estimator 134, a smoothing correlator 152, and an audio instruction determination unit 166. The power estimator 134 includes a delay 136, a delay 138, multipliers 140 and 142, an adder 144, a value 146, and a low-pass filter 148. The smoothing correlator 152 includes a delay 154, multipliers 156 and 158, low-pass filters 160 and 162, and an oscillator 164. The memory 150 is coupled to a delay 136, a delay 138, a low-pass filter 148, a delay 154, low-pass filters 160 and 162, and an oscillator 164. Delay 136 receives Rin 44 or Sin 38 and is coupled to delay 138 and multiplier 142. Delay 138 is coupled to multiplier 140, which also receives Rin 44 or Sin 38. The adder 144 is coupled to multipliers 14 and 142 and a value 146, which is coupled to a low-pass filter 148 (its audio instruction determining unit 166). Delay 54 receives Rin 44 or Sin 38 and is coupled to multiplier 156. Multiplier 158 also receives Rin 44 or Sin 38 and is coupled to low-pass filter 160, oscillator 164, and multiplier 156. The multiplier 156 receives the delay 154 and is coupled to the low-pass filter 162 and the oscillator 164. The audio instruction determination unit 166 receives Ro (n) from the low-pass filter 160 and RJn from the low-pass filter 162), and provides an audio indicator signal 168 to the adaptive filter 28. It should be noted that a concrete embodiment of the blocks in the communication system 10 and the echo canceller 20 of Figs. 1 to 8 is shown. The alternative specific embodiment may include 86017 -13-200417167 as various functionalities, other than the described elements, more than the described elements, or less than the specified elements, as required. In addition, the blocks in Figures 1 to 8 can be divided into different groups or different connections while still achieving the same result. Figures 8 through 8 are therefore only interpreted to provide examples to illustrate the concepts discussed below. Similarly, although the connections in Figures 8 through 8 are single conductors (unidirectional or bidirectional) or multiple conductors (unidirectional or bidirectional) in the drawing, various connections can be used. For example, multiple conductors can be replaced with a variety of different single unidirectional or bidirectional conductors. Similarly, a single conductor can be extended to multiple unidirectional or bidirectional conductors. The 仏 can communicate continuously through a single conductor or in parallel through multiple conductors. Similarly, signals can be time-multiplexed by a single or multiple conductors. Thus, the connections illustrated in Figures 1 to 8 can be implemented in a variety of different ways while still achieving the required functionality. Also, as will be explained further below, the designs of Figures 1 through 8 can be implemented as hardware, software, or a combination of hardware and software. Operation: The transmitter / receiver 12 provides the data signal to the merger 6 and receives the data number from it. Union 16 provides four-wire to two-wire conversion between transmitter / receiver and communication network 24. Therefore, the transmitter / receiver 12 may be any device used to communicate on the communication network 24, such as a telephone or modem, which is coupled to the union 16 via a two-line subscriber line. Therefore, merging 16 provides an interface between a local user loop (with transmitter / receiver 12) and a communication network (communication network 24). Transmitter / receiver 14 and merge 18 function similarly to transmitter / receiver 12 and merge 16 respectively. In the communication between the transmitter / receiver 12 and the transmitter / receiver 14, electronic or linear echoes can be introduced into the communication through union 16 and union 18. The source of this echo is the impedance mismatch in the 16 combined and the impedance mismatch in the 18 combined. For example, if the 86017 -14-200417167 combination 16 impedance is perfectly matched, the entire energy from the received signal Rom 4o will be transmitted to the transmitter / receiver 12. However, if any impedance mismatch occurs at 16 when combined, part of the energy from the received signal Rout 40 will be reflected back by the transmitted signal 37. If the communication network 24 two-way delay (from the transmitter / receiver i4, in the case where the echo is introduced by merge 16) is long enough, the reflected echo from Sin 37 received by the transmitter / receiver 14 is in the communication process Is very significant. This can cause significant echo or even unbearable interference in telephone voice communications. In a specific embodiment, a sufficiently long delay refers to a two-way delay greater than 40 milliseconds. As the round trip delay increases, the echo becomes worse and thus more significant and disruptive. (On the other hand, if the two-way delay is small enough, since the echo cannot be distinguished from the sidetone, it may not be destructive.) The two-way delay may include various delays or a combination thereof, including transmission delay, processing delay, calculation delay, etc. Wait. Depending on the communication system, the two-way delay can be large enough to interrupt communication. Therefore, the echo cancellers 20 and 22 can be used to reduce the linear echo in the communication system 10. For example, from the signal received by Rout 40 (from the transmitter / receiver 4) and reflected back by Sin 37, the echo introduced by merge 16 is processed by echo canceler 20, and the signal Sout 42 is processed by The communication network 24 reduces reflected echoes before transmitting back to the transmitter / receiver 14. As mentioned above, the linear echo is introduced by the impedance mismatch 6 and the merge is introduced by the impedance mismatch 18. Similarly, acoustic echoes can be introduced into the communication via the transmitter / receiver 12 and the transmitter / receiver 14. For example, if the transmitter / receiver 12 is an amplified telephone, the received signal will bounce in the surrounding environment after being output by the speaker. Some signals can be redirected back to the microphone of the transmitter / receiver 12 and also reflected back to the transmitter / receiver. Receiver 14. In a specific embodiment, the function of echo cancellation 86017 -15- 200417167 can reduce acoustic echo in addition to linear echo in some aspects. In a specific embodiment, the communication network 24 may include a packet telephone network (Including, for example, internet protocol (IP) voice, packet data, asynchronous transfer mode (ATM), etc., can be applied to wireless or line systems) or public switched telephone network (Public Switching Telephone Network ; PSTN). In an alternative embodiment, the communication system 10 may refer to any type of communication system. Any communication path can be used as the interface 13 or the interface 15. Control 17 provides control paths in transmitters / receivers 12 and 14, merging 16 and 17, echo cancellers 20 and 22, and communication system 24. The control signal transmitted by the control 17 is usually an off-line signal. For example, the control 17 may include an on / off signal to activate or deactivate the echo canceller 20 or 22. The control 17 may include a signal to indicate that the telephone system is in a hung state or a picked up state. In the specific embodiment described herein, the transmitter / receiver 12 is referred to as the near end of the echo canceller 20, and the transmitter / receiver 14 is referred to as the far end of the echo canceller 20. Therefore, the specific embodiment herein will be described with reference to the echo canceller 20, but it should be understood that the operation of the echo canceller 22 is similar to that of the echo canceller 20. That is, in an alternative embodiment, the transmitter / receiver 14 is referred to as the near end of the echo canceller 22, and the transmitter / receiver 12 is referred to as the far end of the echo canceller 22. FIG. 2 is a specific embodiment of the echo canceller 20, wherein, as described above, the transmitter / receiver 12 is the near end and the transmitter / receiver 14 is the far end. Sin 37 is a transmission signal transmitted from transmitter 12 through merging 16. The echo canceller 20 86017 -16- 200417167 provides the echo canceled transmission signal Sout 42 to the receiver 14 through the communication network 24 and the merger 18. Rin 43 is a reception signal received from transmitter 14 through merging 18 and communication network 24. The echo canceller receives Rin 43 and supplies this transmission signal Rin 43 to receiver 12 in the same way as Rout 40 by merging 16. As mentioned above, Sin 37 may include a reflection echo of merge 16 introduced by impedance mismatch. Therefore, the echo canceller 20 reduces (or eliminates) the reflected echoes introduced and provides an echo canceled transmission signal Sout 42. That is, if the impedance in Merge 16 is perfectly matched, due to no reflection echo (ideally and practically impossible), the signal received at the input of Merge 16 (such as R0Ut 40) will not substantially affect Merge 16 (Sin 37) react. However, if the merger is in an unbalanced state (usually, for example, an impedance mismatch), the signal received by Rout 40 results in the response shown in Figure 37. Figure 37 illustrates the corresponding impulse response (h) of the merging circuit seen from the viewpoint of the input (Rout 40) and the output (Sin 37). The adaptive filter 28 within the echo canceller 20 tries the "analog" Sin 37 (to any input signal Rout 40) and subtracts it by an adder 34. It should be noted that the signal Rout 40 is linearly distorted (including timely pure transposition, that is, shifted in time by a parameter called pure delay). This distortion can be explained by the impulse response of Fig. 37 and 16. It should be noted that the impulse response includes a pure delay portion and a dispersion time. Pure delay refers to the part of the pulse response that appears from the beginning to some significant value, as shown by T1 in Figure 37. Dispersion time refers to the part of the duration of the impulse response from the beginning of the significant response to the substantial disappearance of the response, as shown by Ding 4 + Ding 2 in Figure 37. The impulse response < shape (according to the portion of the corresponding dispersion time zone) can be converted to a merged frequency characteristic (as seen from the Rout 4 / Sin 3 7 input / output ports).

The Sm 37 is provided to the DC notch wave filter 45, and the DC 86017 -17- 200417167 component is removed from the Sin 37. It should be noted that in an alternative embodiment, a high-pass filter may be used for the position of the DC notch wave filter 45. Similarly, the output (Rout 40) of the adder 36 is provided to a DC notch wave filter 49 'to remove the DC component from the ROUt 40 (however, a high-pass filter may be used instead of the specific embodiment). The use of a DC notch / wave filter is less expensive than a two-pass filter in terms of lift and does not cause ripple effects. It helps to maintain gain flatness through the passband of the filter. In an alternative embodiment, a single common DC notch wave filter may be used to perform the functions of the DC notch wave filter 45 and the DC notch wave filter 49. It should be noted that the adder 36 receives Rin 43 and the serial signal 41 and provides the sum of the two signals Rout 40. However, if the serial signal 41 is zero, the output Rout 4 is the same as the input Rin 43. For the following description, the serial signal 41 is assumed to be zero and Rout 40 is equal to Rin 43. Also, pay attention to the selectivity of the non-adaptive filter 31 and the non-adaptive filter 35, which will be described further below. For the following description, it will be assumed that Sin 38 and Sill 39 are equal, and error signal 47 is a gain-adjusted version of error signal 46 without the effect of non-adaptive filter 35. Therefore, Sin 39 is a transmission signal that includes any Proximity (Sgen) transmitted by transmitter 12 and any reflection echo introduced from Rout 40 by merging 16. Therefore, 'Sin 39 can be expressed as "Sgen + Echo". The adaptive filter 28 provides an estimate of the reflected echo, i.e. the echo estimation signal 48 to the adder 34 'which outputs an error signal 46. Therefore, the error signal 46 can be expressed as "Sin 39-estimated echo 48" or replace sin 39 with the above-mentioned calculation formula, that is, "Sgen + echo-estimated echo 48". When the estimated echo is accurate (that is, equal to or substantially equal to the actual echo), the error signal 仏 will only include Sgen without any real 86017 -18-200417167 prime echo. This is the ideal situation. However, if the estimated echo is accurate, the error signal 46 will include Sgen and the residual echo components. In this case, the error signal 46 can be expressed as "Sgen + residual echo", where the residual echo ^ "echo-estimated echo". When there is no Sgen (i.e., when the near end is silent, meaning that the signal from the transmitter 12 is not transmitted), the error signal 46 only represents the residual echo. In this case, the error signal 46 may be used to perform an adaptive method to minimize residual echoes, as described in further detail below. However, if there is, the error signal 46 cannot be used to perform the adaptive method, because the adaptive filter 28 uses the error to adapt, the error signal 46 cannot adjust the error in the presence of Sgen. Therefore, Sgen needs to be checked to determine whether the adaptive method can be performed. The near-end signal detector 26 coupled to receive Sin 38 (which is equal to sin 39 in this example) and Rin 44 uses an error signal from the monitoring and control unit 30 and a control signal to detect the presence of Sgen (ie, to detect transmission The presence of a near-end talker on the device 12). In the adaptive filter unit 28, the echo estimation signal 48, y (k) is calculated by y (k) = XT (k) · H (k), where X (k) = [x (k), x (k _1), ..., x (k _ N + 1) is an input signal vector that extends to the duration of the FIR filter range; x (n) = Riri44. H (k) is the filter coefficient vector for the k-th iteration, where H (k) = [h0 (k), hKk), ...,! ^-#)] 1. The actual update of the filter coefficients is controlled by a general LMS algorithm. H (k + l) = H (k) + step-size 4 ιτογ (1 < :) × () < :) 'Where error (k) corresponds to the error signal 46; stepjize controls the adaptation rate; and H (k + 1) is the new filter coefficient vector. Any residual echo in the error signal 46 can be further reduced or removed by the non-linear processor 32. The non-linear processor 32 receives an error 86017 -19- 200417167 error signal 47 (a gain-adjusted version of the error signal 46 in this specific embodiment) and a control signal from the monitoring and control unit 30 to generate Sout 42, which is not ideal in the ideal situation. Include echo. In addition to reducing or removing residual echo, the non-linear processor 32 also attempts to maintain or match the background noise of the near-end talker signal (Sgen). Matching background noise improves communication quality by maintaining continuity of real background noise. Without continuity, 'the far-end listener cannot hear the near-end talker during the far-end conversation. Alternatively, there may be synthetic background noise in the far-end conversation, however, this may lead to a destructive exchange between real-world noise (in the near-end conversation) and synthetic background noise (in the far-end conversation). Therefore, matching background noise helps to minimize this disruptive exchange. The monitoring and control unit 30 includes a wavelet coefficient monitor (such as the filter coefficient monitor 102, which will be further explained with reference to FIG. 6), which is used to determine whether there is a correct merge so that the adaptive filter 28 does not attempt to adapt to invalidity Merge. The monitoring and control unit 30 also includes a gain monitor for controlling the gain control 33 in the selective adaptive filter 35. The purpose of the gain control 33 is to maintain the stability of the communication system 10. The rate monitoring and control unit 30 for improving the adaptive filter 28 also includes a pure delay determiner and a sparse window positioner (both will be described in detail with reference to FIG. 7). The monitoring and control unit 30 also includes an audio indicator and an audio detector (which will be described in detail with reference to FIG. 8). The audio indicator and audio detector can be used to detect the signal audio in the communication system 10. For example, these signaled audios may include a 200 edge audio with phase inversion to turn off the echo canceller when transmitting data after the signaled audio. Therefore, the echo canceller can be turned off as needed. On the other hand, if the adaptive filter 28 is exposed to the audio 86017-20-200417167 (e.g., single or multiple sinusoidal frequencies) transmitted by the transmitter 12 or the transmitter 14, the communication system i stability. Therefore, audio detection can be used to prevent the adaptive filter from diverging and causing instability. In the above specific embodiment, the echo canceller 20 does not include the non-adaptive filters 31 and 35. However, in a specific embodiment, the non-adaptive filter 3 coupled between the DC notch wave filter 45 and the adder 34 can be used to reduce the length of the adaptive wave filter 28 (further referring to FIG. 4 Description). In this embodiment, the 'non-adaptive waver 31 receives Sin 38 and a control signal from the monitoring and control unit 30 to generate Sin 39. Similarly, in a specific embodiment with a non-adaptive filter 31, the echo canceller may also include a non-adaptive filter 35 coupled between the adder 34 and the non-linear processor 32. The non-adaptive filter 35 may include a gain control 33 or may be a separate unit. In this embodiment, the 'non-adaptive filter 35 compensates for the effect of the non-adaptive filter 31 so that the near-end signal Sgen is not distorted. The non-adaptive filter 35 receives the error signal 46, the control signal from the monitoring and control unit 30, and provides the error signal 47 to the non-linear processor 32. (Non-adaptive filters 3 丨 and 35 will be further explained with reference to FIG. 4). The monitoring and control unit 30 for injecting a signal into Rin 43 to generate R0Ut 40 will also provide a serial signal 41 to an adder 36. The injection of the serial signal 41 can be used to estimate the pure delay of the merge echo path (the path from Rout 40 through merge 16 to sin 37). Pure delay refers to the minimum time delay from Rout 40 to Sin 37. The injection of the tandem signal 41 can be used to estimate the pure delay when there is no remote signal at the beginning of the communication (e.g. at the beginning of a telephone session). It should be noted that the serial signal 41 is optional. The monitoring and control unit 30 can also receive control 17 to enable or disable the function 86017 -21-200417167 All or part of the module. FIG. 9 includes a flow 200 that illustrates the operation of the echo canceller 20 according to a specific embodiment of the present invention. Process 200 is an overview of the functionality provided by an echo canceller (e.g., echo canceller 20 of FIG. 2). Each step in the process 200 will be described in detail with reference to FIGS. 3 to 8 and 10 to 38 below. The process 200 starts at 202 9 and proceeds to block 204, where DC notch wave filtering is performed on both Rin and Sin. It should be noted that if there is an adder 36 or a tandem signal 41, the DC notch wave filtering is performed on the output (Rout 40) of the adder 36 instead of Rin 43. As described above, the DC notch wave filter 45 removes the DC components from Sin 37 and generates Sin 38. Similarly, the DC notch wave filter 49 removes the DC component from Rin 43 (or Rout 40, depending on the serial signal 41) and generates Rin 44. Flow 200 then proceeds to block 206 where the long-term power of Rin 44 and the short-term power of Sin 38 are estimated. It should be noted that long-term power and short-term power are relative periods. That is, long-term power refers to power measured over a longer period of time than short-term power. These powers can be calculated by the near-end signal detector 26 of the echo canceller 20. The power calculated in block 206 is then used to determine the near end talker signal detection (NESD) limit. This NESD limit is then used to determine the presence of the near-end talker signal (ie, Sgen). This decision can also be performed by the near-end signal detector 26 of the echo canceller 20. Flow 200 then proceeds to block 210 where the adaptive filter 28 is monitored and controlled. Block 210 includes blocks 209, 211, and 213. It should be noted that the function of monitoring and controlling the adaptive filter 210 is selective. That is, any combination of blocks 209, 211, and 213 can be executed, or none of them can be executed. In block 209, audio instruction processing is performed. This audio instruction processing can be executed by the monitoring and control unit 30. 86017 -22- 200417167 is as described above with reference to FIG. 2, and will be further described below with reference to FIG. 8. The process 200 then proceeds to block 211, where the delay is detected (pure delay in a specific embodiment), and a filter window (sparse window) with the correct size is located. That is, the monitoring and control unit 30 can detect the delay and locate the sparse window, thereby reducing the length (i.e., the number of taps) of the adaptive filter 28. Another method to shorten the length of the adaptive filter is done by blocks 2 丨 3. A specific embodiment is a combination of the non-adaptive filters 3 and 33 and the adaptive filter 28, but the filter length is short. Figures 28 to 35 will provide detailed explanations. After monitoring and controlling the adaptive filter 210, the process 200 proceeds to block 2J2 'where the adaptive filter is used to generate an echo estimation signal. For example, this may correspond to an adaptive filter 28 that generates an echo estimation signal 48, as explained above with reference to FIG. The process 200 then proceeds to block 214 where the error signal and the short-term power of the error signal are estimated. That is, block 214 may correspond to the adder 34 of FIG. 2 which estimates the error signal 46 by subtracting the echo estimation signal 48 from Sin 39. The monitoring and control unit 30 can then be used to estimate the short-term power of the error signal 46. The flow then proceeds to block 216 where the near-end talker signal is detected using the NESD limit. That is, in block 216, the presence or absence of Sgen * is detected (whether a signal is transmitted from the transmitter 12 of FIG. 1). This can be performed using the near-end signal detector 26 of FIG. The flow proceeds to block 218, where the gain of the gain control 33 is detected and selectively adjusted to maintain the stability of the communication system 10 and the adaptive filter 28 (described in detail below). The process 200 then proceeds to a decision diamond 220, where it is determined whether the filter coefficients need to be updated. For example, as described above, if l§gen exists, the error signal 46 includes the near-end talker signal (sgen) and the residual echo group 86017 -23- 200417167. In this case, the adaptive filter 28 should not be updated because the error signal 46 does not represent the residual echo just now. The flow goes to decision diamond 224. However, if it is determined that Sgen does not exist (ie, the near-end talker is silent), the adaptive filter is "updatable" and the flow proceeds to block 222, where the filter coefficients of the adaptive filter 28 are updated before proceeding to the decision diamond m. At the decision diamond 224, it is determined whether background processing is required. In a specific embodiment, the side processing is performed periodically during the operation of the echo canceller 20. In alternative embodiments, it can be completed at different times, for example according to various adaptations If the background processing is not performed, the flow proceeds to step 230, where non-linear processing is performed. However, if the background processing is performed, the flow proceeds to block 226, where the filter coefficients are backed up, that is, the filtering of the adaptive filter 28 The filter coefficients can be stored (for example, in a storage unit, which can be located in the echo canceller 20 or the echo canceller 2). The process then proceeds to the block ^ 'where the wave filter coefficients are detected to determine whether there is an echo canceler. Controller stability control. ^ After processing, if any, the process enters non-linear processing, which reduces or removes any remaining residual The remaining echoes and background noise are inserted as needed. If there are more samples received by Rin43 and Sin37 (located at the decision diamond 232), return to the sample under block 204 to continue processing, otherwise the process is completed at the end of Magic 4. It should be noted that In a telephone application, since the signal usually includes a voice signal, the sampling rate is usually 8 kHz. Therefore, in a specific embodiment, the sampling rate is 8 kHz, and samples like ^ are received every 0.125 post. However, instead In a specific embodiment, different sampling rates may be used. For example, a higher sampling rate is required for private applications. In addition, in digital applications 86017 -24-200417167, the fetch rate can be determined by the transmission rate of the secret digital information. It should be noted that the steps shown in FIG. 9 represent one embodiment of the present invention which can be executed in various different orders ..., embodiments. Instead of executing the steps with a U.S. sequence, some steps can be performed in the most, the fewer Or performed concurrently with other steps. Similarly, some steps of the process may be optional, and other embodiments may use additional or different steps to perform any desired operation. Therefore, There may be many changes for the technicians in Yuebai. The flow 200 is only an example of the operation of the echo canceller. Similarly, the “echo canceller 20” also only describes one possible specific embodiment. Alternative specific embodiments may use more Or fewer blocks or units that perform all, part, or even different functions described in FIG. 2. Therefore, the echo canceller 20 of FIG. ^ Should only be regarded as an example. Note also the blocks of FIG. 2 and the steps of FIG. 9骡 All software running on data processors (such as microprocessors, digital signal processors, etc.) can be performed by hardware or by a combination of hardware and software. Figure 3 is near-end signal detection A specific embodiment of the detector 26. The operation of the near-end signal detector 26 will be described with reference to Figs. 10 to 13. The processes of the near-end signal detector 26 and Figs. 10 to 13 provide fast and reliable detection, and It will not be affected by the echo path delay and echo return loss (ERL). It is the signal attenuation of the echo canceller Rout port to Sin port due to transmission and merging loss in the echo path. As mentioned above, when detecting near-end talker signals (that is, when detecting the presence of Sgen), in order to prevent the adaptation from diverging, the adaptation method (to reduce the average power of the residual echo to a minimum affects the coefficient of the adaptive filter 28 ), Because the presence of the near-end talker signal indicates that the error signal 46 is not an error caused by the echo alone. It should be noted that when detecting the near-end signal, adjust 86017 -25- 200417167 to terminate it properly, regardless of whether the near-end signal is in a single conversation situation (that is, there is only one near-end talker) or a dual-talk situation (when the near-end talker and remote End talkers are sometimes). In addition to the termination adaptation method, the filter coefficients may need to be recovered from the backup filter coefficients. In addition, when there are no near-end and far-end signals, the adaptation method is also discontinued to prevent the echo canceller 20 from adapting to channel noise or low error signals, thereby minimizing the calculation. Therefore, the echo canceller 20 is adapted for operation when needed ', for example when the far-end signal is relatively strong and there is no near-end signal. In this case, the adaptive filter 28 can be adapted to use the correct estimate as the estimated signal 48 echo. Similarly, as will be explained below, the near-end talker signal detection limit is "shifted" (that is, adjusted) and depends on the state of the adaptive filter method. The specific embodiments illustrated in FIGS. 10 to 13 also provide a method for backing up and restoring the adaptive filter 28 coefficients. This method can be controlled by a state machine, as illustrated in Figure 13, which minimizes the number and frequency of backups and prevents the adaptive filter 28 from diverging. FIG. 3 shows a specific embodiment of the near-end signal detector 26. The signal level estimator tracks the level of the near-end signal (Sgen), the far-end signal (Rin), and the transmission path input signal (Sin). Therefore, the near-end signal level estimator 50 receives an error # 46 ', and the far-end signal level estimator 52 receives Rin 44 and the sin signal level estimator 54 receives Sin 38. The signal level estimator is then used to control a near-end signal detection (NESD) limit selector 58 and a near-end signal detector 60. The background processor 56 monitors the processing status of the adaptive filter 28 and controls the NESD limit selector 58 and the near-end signal detector 60. It should be noted that usually a low-pass filter can be applied to the measurement signal at each signal level 86017 -26- 200417167 ′ to estimate the power or value. Similarly, the descriptions in Figures 3 and 10 to 13 below assume that the signal is sampled at 8 kHz (as described above, which is the common rate for normal speech applications).

A specific embodiment of the Sin signal level estimator 54 uses the following formula to obtain the power (Psin) of Sin.

Formula 1: Psin (n) = [(N -l) PSin (n _l) + (Sin (n)) 2] / N In the above formula, Sin (n) is the transmission of the input echo canceler 20 at time η. Path 'Psin (n) is the estimated transmission path input signal power at time η, and ^^ is a smoothing factor, which is assumed to be 32 in a specific embodiment. Instead of a specific embodiment, a range of N values may be used. In general, ν should be chosen large enough so that the sin power estimate is not too sensitive to rapid changes in Sin. On the other hand, n cannot be large enough to make Sin's power estimation sensitive enough to track the speech signal level, and the power estimation delay is minimized. Alternatively, the power can be estimated using a moving average method with a window size of itself. It can be seen in the figure that this method provides the same bandwidth as the power estimator according to Equation 1. The near-end "level estimator 50 receives the error signal 46 and obtains the near-end signal power at time n. Although described above, there is no direct measure of the near-end L number (Sgen) of the echo canceller 20. That is, Sin 38 is a mix of Sgen and reflected echo from Rin 44. Therefore, a specific embodiment of the near-end signal level estimator 50 uses Sin 39 (which is a filtered version of Sin 38, assuming that the filter is located in a DC notch wave) The difference between the filter 45 and the adder 34 in FIG. 2) and the echo estimator signal 48. Therefore, the error signal 46 is provided to the near-end signal level estimator 50. The error signal 46 is an echo canceller 20 available with Sgen Is closest to the estimate, but the accuracy of this estimate is the function of the convergence state of the adaptive filter 28 86017 -27- 200417167 state. Ideally, it is the estimate of the echo when the adaptive filter is fully converged (echo estimation signal 48) Is accurate. In practice, as described above, the echo estimation signal 48 is usually not equal to the reflected echo from Rin 44, so the error signal 46 is not only sgen, but Sgen + residual echo. Since the adaptive method continues at a time-specific window The errors introduced by the residual echo are minimized. Therefore, a specific embodiment of the near-end signal level estimator 50 uses the following formula:

Equation 2 · Perror (n) = [(N _l) Perror (n _l) + (error signal 46) 2] / N In the above formula, the error signal 46 is between Sin39 and the echo estimation signal 48 output from the adder 34 Perror (n) is the estimated near-end signal power at time n, and N is the smoothing factor of the estimator (32 in the current specific embodiment). 〇 Obtained in a specific embodiment of the far-end signal level estimator 52. Rin is a short-term power and uses it to calculate Rin—the average Rin power of some past short-term power estimates' and its coverage of the echo path. For example, a specific embodiment uses the following formula to determine Rin's short-term power: Formula 3 ·· PRin (kN) = ^^ (Rin (kN — 1, 2, · · · ι · = 0) In the above formula, Rin (kN -i) is the receiving path of the input echo canceller 20 at time kN-i, PRin (kN) is the estimated far-end signal power at time kN (it should be considered to reduce the calculation cost. The estimated pRin (kN) is every N samples instead of each sample). N is the window size (32 in a specific embodiment). Therefore, Equation 4 calculates the power of Rin every N samples in the current window (size N), where & Window track. That is, the first window (k = 1) can be defined by samples 1 to 32, the next window ^) can be defined by samples 33 to 64, etc. Therefore, the average power of the far-end signal is 86017 -28- 200417167 rate. Formula 4: AVG P = (shape) = Shi ζρGa (bu-㈣k = 1, 2, ... In the above formula, "PRin ((k · i) N) is the far-end signal power estimate at time (ki) N A snapshot of the past M, where i = M -1, M -2, ..., 0. AVG PRin (kN) is the average value of the far-end signal level estimate at time kN, μ is the window size of this average, where M = 16, The echo canceller is designed to cover echo path delays up to 64 ms (ie M * Ν = 16 * 32 = 512 samples). For example, if the current window is the 16th window (ie k = 16), then AVG PRin The (kN) value is the average value of PRin (kN), and its calculation is used for every 16 previous windows, namely pRin (16 * N) > PRi „(15 * N) ..... PRin (2 * N ), The average value of PRin (N). If there is not enough previous data (that is, fewer than M processed windows), only those available values can be used to determine the average value and zero can be used for values that have not yet been calculated. For example, for In the third window (k = 3), only two previous pRin (kN) values are available. Therefore, AVG PRin (kN) is the average of the 3 values, not the M value. It should be noted that AVG pRijkN is every N samples Calculation, as seen with reference to FIG. 10, after calculating AVG Pb (kN), the value of k may be incremented to indicate the start of the next N sample windows. In an alternative embodiment, the far-end signal level estimator 52 may use the above formula a 1 or 2 (where n = 256) is used to estimate the average Rin power. Using Equation 4, AVg PRin (kN) measurements should also be taken every 32 samples. It should also be noted that the above level estimation can be done using values instead of power. Similarly , Note that the above formula ⑴ deals with data at a nominal rate, while formulas 3 and 4 perform subrate # calculations (thus performing calculations every _ samples) in continuous N-size windows. Alternative specific embodiment 1 constitutes the above formula and is not limited to The formula provided above. 86017 -29 · 200417167 The near-end (Sgen), far-end (Rin), and transmission path input (Sin) signal level estimates are used to control the detection of near-end talker signals. Therefore, FIG. 10 is a specific embodiment of blocks 206 and 208 in FIG. 9, in which the near-end signal detection limit (NESD_Threshold) is determined. The limit is re-evaluated every N samples, where N is 32 in the current embodiment. In block 250, the short-term power of Rh and PRin is determined (for example, see Equation 3 above), where the current time η corresponds to the current sample being analyzed. In block 252, the sample counter is incremented to provide a new value of n. Therefore, the sample is incremented each time it passes the flow 200 of FIG. 9 (that is, each time it passes 206 and 208). The flow proceeds to decision diamond 254, where it is determined whether the sample counter has reached the window size N. If not, the flow proceeds to block 270 (note that NESD_Threshold has not been updated). The power of Sin (Psin) is calculated in block 270 (see, for example, Equation 1 above). If the sample counter has reached N, the flow proceeds to block 256, where the counter is reset so that the path of blocks 256 to 268 is taken every N samples (in a specific embodiment, every 32 samples, N equals 32) . After resetting the sample counter (resetting to zero in a specific embodiment), the flow proceeds to block 258, where the average power of Rin, AVG PRin, is calculated (for example, see Equation 4 above). After that, the flow proceeds to a decision diamond 260, in which it is determined whether BACKUP_STATE is 0 or 1 (note that BACKUP JTATE will be explained in detail with reference to FIG. 13). If yes, the flow proceeds to block 262, where K1 is used to adjust the NESD_Threshold shown in Equation 5 below: Equation 5 ·· NESD_Threshold = K1 * AVG PRin However, if BACKUP_STATE is not 0 or 1, the flow proceeds to block 264, where K2 is used The NESD_Threshold shown in Equation 6 is adjusted as follows: 86017 -30- 200417167 Equation 6: NESD_Threshold = K2 * AVG PRin Therefore, NESD_Threshold is re-evaluated every N samples, depending on the state of the adaptive filter (ie BACKUP_STATE, corresponding to the state machine, which will be further explained with reference to FIG. 13), NESD_Threshold can be set to K1 * AVGPRini ^ K2 * AVGPRin cKl and K2 are scale factors of the NESDP root value. At the initial phase of the adaptation method of the adaptive filter 28 (that is, when BACKUP_STATE is 0 or 1), NESD_Threshold can be relatively large, providing more adaptive opportunities for the adaptive filter 28. On the other hand, when the adaptive filter 28 has passed the initial adaptation phase (ie, when B ACKUP_STATE is 3 or 4), the NESD_Threshold can be reduced to prevent the adaptation method from diverging. In a specific embodiment, K1 is set to a value in the range of 1 to 2, and K2 is set to a value in the range of 0.25 to 1. For example, in a specific embodiment, according to the merging condition, K1 is 1 and K2 is 0.5. In addition, these K1 and K2 values can be set statically or dynamically in the adaptive method. Alternate values outside the given range can be used. The adaptation method is still in the initial phase and can be determined using other methods than the 4 state (eg BACKUP_STATE0 to 3) state machine. After adjusting NESD_Threshold at block 262 or 264, the flow enters decision diamond 266, which determines whether NESD_Threshold is less than A. If not, the process proceeds to block 270 where Psin is calculated. If so, set NESDJThreshold to A at block 268. That is, in the case where the AVG PRin is too small, NESD-Threshold is limited to the minimum level of A (where A can correspond to a value in the range of -40 to -45 dBmO in a specific embodiment). The flow then proceeds to block 270. After the process of FIG. 10 is completed, monitoring and control of the adaptive filter 28 is performed as needed (see block 210 of FIG. 9). (It should be noted that 86017-31-200417167 below blocks 209, 210, and 211 will be described in detail with reference to Figures 20 to 34.) After the adaptive filter at block 212 generates an echo estimation signal 48, the flow proceeds to blocks 214 and 216, which will refer to the figure. 11 Further details. That is, the flow chart of FIG. 11 illustrates a part of blocks 214 and 2 16 of FIG. 9 ′, which details the control of signal detection of the near-end talker (ie, detection by Sgen). The power (Perror) of the error signal 46 is estimated in block 276 (see Equation 2 above). The flow then proceeds to a decision diamond 278, in which it is determined whether the smaller of Perror and PSin is greater than NESD_Threshold (ie, whether MIN (Perrc) r, psin) > NESD_Threshold). If yes, the flow enters the decision diamond 280, which determines whether the NESD_Hangover timer counts down to zero. If completed, the near-end signal is detected. That is, the near-end signal is detected only when MIN (Perror, PSin) > NESD_Threshold and no near-end signal has been detected in the past a specific time window (corresponding to the NESD_Hangover timer). If it is determined that diamond 278, MIN (Perror, PSin) is not greater than NESD_Threshold, the flow proceeds to block 290, where the value of the NESD_Hangover timer is decremented to zero, thereby introducing a pause determined by the NESD_Hangover time. If the diamond 280 is determined and the NESD_Hangover timer is non-zero, the NESD_Hangover timer is set to a predetermined value in block 286. If the near-end signal (Sgen) has been detected, the process proceeds from decision diamond 280 to decision diamond 282, where it is determined whether the filter coefficients have been updated. If so, it is assumed that these coefficients are usually likely to be corrupted due to the presence of the near-end signal. That is, because the signal used for coefficient update is no longer a pure residual echo, but a mixture of residual echo and Sgen, these coefficients no longer represent the estimated echo. In this case, the flow proceeds to block 284, where the wavelet coefficients are recovered or replaced by a set of filter coefficients that have been proven to be "good" 86017 -32 · 200417167. The method of backing up and restoring the filter coefficients will be described below with reference to FIGS. 12 and 13. The flow then proceeds to block 288, where BACKUP_STATE is updated. If the filter coefficients are not updated in the decision diamond 282, they will not be assumed to be corrupted because they have not yet used residual echo and Sgen's hybrid adaptation. In this case, the flow proceeds to block 286, where the NESD_Hangover timer is set to a predetermined value. The duration of the NESD-Hangover time for the NESD-Hangover timer is selected to ensure that Sgen no longer appears before the start of the filter coefficient adjustment, and to avoid unwanted filter coefficient adjustment and recovery. For example, in a specific embodiment, the NESD_Hangover time is 160 samples, or 20 milliseconds. Therefore, the duration of the NESDJ Hangover time prevents the near-end signal detector 26 from being too sensitive, thereby minimizing the exchange between the detection of the near-end talker signal and the lack of detection of the near-end talker signal. However, if the NESDJ Hangover time is set too long, the near-end signal detector 26 may not be sensitive enough to accurately detect the near-end talker signal when needed. Therefore, under different combinations of signal levels (ie, power) of Sgen and Rin, different actions regarding filter coefficients (such as the coefficients of the adaptive filter 28) are used. For example, these actions can be summarized using the following table. Item Sgen Sin Rin Action description 1 Low Low Low No update No near-end or far-end talker signal appears 2 Low Low High update coefficient Single remote talker signal with a large amount of combined attenuation 86017 -33- 200417167 3 Low High Low n / a Inefficient combination 4 Low, high and high update coefficients Single remote talker signal with a small amount of combined attenuation 5 High and low low n / a Ineffective combination 6 High and low high n / a Ineffective combination 7 High and low freeze update and restore filter coefficients Single Near-end talker signal appears 8 high) East end update and restore filter coefficients double dialogue (both near-end and far-end talker signals appear) Table 1 Because Sin is a mix of Sgen and echo or Rin, listed in the above table A few combinations are not valid combinations in normal operating mode (meaning the connection is not disconnected or there is no external signal injection circuit). Invalid combinations are project 3 (because Sin cannot be high when Sgen and Rin are both low), and projects 5 and 6 (because Sin cannot be low when Sgen is high). Three sets of actions are used for the remaining 5 combinations. First, the state of the coefficient adjustment method is when Rin is high and Sgen is low (in a single remote dialog cycle, whether sin is high or low, it is project 2 and 4 respectively). In these cases, the error (error signal 46) is mainly due to residual echo (due to the low sgen), so that the effect of adaptation on the adjustment is minimal. Second, the condition of terminating the filter coefficient adaptation processor and restoring the previous pre-filled "good" filter coefficients is when Sgen is high (in the near-end dialogue cycle, whether it is a single dialogue or a double dialogue, it is project 7 and 8 respectively) . Second, there is no need to update when both the near-end and far-end talkers are silent (project 丨). When the echo return loss is close to or less than 6 dB, the above method for detecting Sgen provides the ability to cancel the echo. Therefore, when it is close to or less than 6 dB (for example, item 4 in Table 1 of the above-mentioned 86017-34-200417167), the above method uses the minimum value of ⑶ (as described above, it can be estimated as the error signal 46, assuming that the residual error is small or can be Ignore) and the Sin that caused the false detection of these conditions, which is different from the previous solution, which has the serious impact of these lower level (close to or less than 6 dB) echo return loss changes and therefore tends to be practical in Sgen If it does not appear, it is detected by mistake. In addition, when the echo return loss is as high as zero offenses (no merging attenuation), the above method allows the adaptation method to continue, eliminating the echo, unlike the previous solution, where the adaptation method terminates at, for example, the 6 dB level. Similarly, the method described above with reference to FIGS. 10 and 11 provides fast detection of Sgen, even when its level is lower than Rin (corresponding to Project 8 in Table i above). For example, the previous solution set the dual dialogue detection limit to a Sin energy greater than or equal to 1/4 of the Rin energy (that is, corresponding to the 6 昍 loss introduced by the merger circuit). If the combined attenuation is 10 dB, the 4 dB difference within the detection limit will be large enough to allow a large number of Sgen signals to be detected without being detected as a dual conversation. As a result, these previous solutions were not always able to detect the near-end call signal, or the detection was too late. The above method uses the minimum value of Sgen (which can be estimated as an erroneous signal as described above, assuming that the residual error is small or negligible), compared with Rin, which is used for near-end signal detection, the detection limit (NESD-Threshold setting) It has nothing to do with the loss of echo return that results in faster and more reliable near-end signal detection than previously available solutions. In addition, the method described above with reference to Figures 10 and 1 丨 provides the ability to distinguish between dual conversations with some noise background (the table above) Project 8) and a single remote conversation (Project 4 in Table 1 above). When the near-end background noise level is high, the previous solution detected this situation as a dual conversation and terminated the adaptation method. Due to background noise It can exist for a long time, even for the entire phone call duration, the adaptive 86017-35-200417167 adaptive filter still cannot change the convergence. Therefore, the above-mentioned near-end signal detection in this paper uses the Sgen minimum value and Sin to Rin to make the detection The limit (NESD_Threshold) can be set so that the adaptive method will continue to perform even when the background noise level is high. (It should be noted that the only correct dual-dialogue status is the Rin and Sgen letters. When the level is high. However, when detecting the signal of the near-end talker, whether or not it is in a single near-end call period or a double conversation period, the adaptation method described in this article should be terminated and the filter coefficients should be restored.) Figure 12 illustrates Figure 9 determines a part of the diamond 224 and the block 226, which determines whether the background method is executed, and if so, backs up the filter coefficients. The process of FIG. 12 mainly deals with the backup policy of the filter coefficients (adaptive filter 28). One of the backup policies The specific embodiment ensures that good filter coefficients are periodically backed up in order to minimize the number of backups and minimize the frequency of backups. Figure 12 starts with block 291, where the background 1 counter is incremented. The process enters a decision diamond 293 'where the background is determined 1 Whether the counter has reached the predetermined counter value, j. If not, the process proceeds to point Η (after block 228 in FIG. 9). If so, the process proceeds to block 298, where the background 1 counter is reset (to zero), and then enters the decision diamond 295. 'Whether it determines whether the filter coefficients of the adaptive filter 28 have been updated. If not, the flow enters point Η. If yes, the flow The process proceeds to block 292, where the background 2 counter is incremented. Then the process proceeds to a decision diamond 294, which determines whether the background 2 counter has reached the predetermined counter value, L. If not, the process proceeds to step Η. If so, the process proceeds to block 296 where the background processing is performed That is, the background processing in this specific embodiment is performed at most every J * L samples, and these values can be set to any value that helps determine the background processing frequency. For example, in a specific embodiment; 86017 -36- 200417167 160 samples, L is 10, where the background processing is performed at most every J * L or 1600 samples. That is, if the filter coefficients of the adaptive filter 28 are not updated after J samples, the flow enters point Η, background 2 The counter does not increment. Therefore, only if the coefficients have been updated in the current window of the J samples, the background 2 counter is incremented and compared with L. In block 296, the background 2 counter is reset (reset to zero in this specific embodiment). Flow continues from block 296 to decision diamond 300. In the decision diamond 300, it is determined whether the current BACKUP_STATE (which will be described in detail with reference to FIG. 13) is 0 or 1. If so, BACKUP_STATE is incremented in block 304 and the flow proceeds to block 308. If BACKUP_STATE is not 0 or 1, the flow enters decision diamond 302, where it is determined whether BACKUP_STATE is 2. If not, the process proceeds to block 306 (indicating that BACKUP_STATE is 3), where BACKUP_STATE is set to 2 and the process proceeds to block 310. If it is determined that the diamond 302 BACKUP_STATE is 2, the flow proceeds to block 308, where the candidate backup coefficient is copied to the good backup coefficient. (Note that candidates and good backup coefficients will be explained below with reference to FIG. 13) Then the flow continues to block 310, where the current filter coefficients are copied to the candidate backup coefficients. That is, at block 308, the candidate backup coefficient becomes a good backup coefficient, and the current filter coefficient becomes a candidate backup coefficient. The current filter coefficient, candidate backup coefficient, and good backup coefficient can all be stored in the echo canceller 20 or outside the echo canceller 20 Storage unit or storage unit in storage location. The flow then proceeds to block 228 in FIG. A specific embodiment of the present invention uses two coefficient backups marked as candidate backup coefficients and good backup coefficients, and has a combination of 4 different BACKUP_STATE (0 to 3). Therefore, FIG. 13 illustrates a state machine that controls backup and adaptive filter coefficient recovery method of 86017-37-200417167 filter 28. The state machine of FIG. 13 includes 4 BACKUP_STATE0 to 3. STATE0 indicates that neither candidate backup factor nor good backup factor is available. STATE1 indicates that candidate backup factors are available and good backup factors are not available. STATE2 indicates that both candidate backup factors and good backup factors are available. STATE3 indicates that the candidate backup factor is not available and the good backup factor is available. It should be noted that the state machine of FIG. 13 implements a portion of blocks 216 and 226 of FIG. In a specific embodiment, the state machine starts with STATE0 upon reset or initialization. If the near-end signal (Sgen) is not detected during the last L items of background processing, the state machine switches to STATE1. Therefore, the minimum time window for the first backup of the filter coefficients is J * L samples (where L is 10 in the current embodiment, and J * K is therefore 1600 samples or 200 ms, assuming a sampling rate of 8 kHz). For this state transition, the near-end signal is still not detected, and a first backup is performed by copying the current filter coefficient to the candidate backup coefficient. According to the detection of the near-end signal (Sgen), the state machine switches back to STATE0, because the stored candidate backup coefficients can be damaged due to the detection delay of the near-end signal Sgen. The state machine remains at STATE0 until the near-end signal is not detected in the last L background processing at this point. As mentioned above, the state machine transitions to STATE1 again. In STATE1 'If the near-end signal is not detected in the other L items of background processing, the state machine switches to STATE2, where a second backup copies the candidate backup coefficient to a good backup coefficient and copies the current filter coefficient to the candidate Backup factor to perform. In this state, both candidate and good backup coefficients are available ’If the near-end signal state is not detected, it will remain in this state. It should be noted that in a specific embodiment, even if the status does not change, the candidate 86017 • 38-200417167 and the Liangzhu backup coefficient in the second backup are updated with continuous backup. Similarly, in a specific embodiment, the two replications performed when the STATE 1 is converted to the STATE 2 are performed by first marking the candidate backup coefficient as a good backup coefficient (for example, by an adaptation indicator) and then copying the current filter coefficient to the candidate backup Factor, which is used to mark it as a good backup factor, and is performed using a single copy. In STATE2, when a near-end signal is detected, the state machine transitions to STATE3, where the candidate backup coefficients are again considered damaged, but good backup coefficients are still considered good 'because these good backup coefficients have been proven to be at least in the j * L time window After still good. The state machine remains in STATE3 as long as there is a near-end signal, or returns to STATE2 if there is no near-end signal. It should be noted that in alternative embodiments, each term of the background processing (L) may occur on every sample instead of every j samples. Similarly, the state machine of Figure 3 can be implemented in a variety of different ways, and can include more, fewer, or different states than those described. FIG. 6 illustrates a specific embodiment of a part of the monitoring and control unit 30 of FIG. 2, which combines the stability of the gain control 33, the control system 10 and the adaptive filter 28 of FIG. 2. For example, if the system generates a continuous unnatural signal due to a set of filter coefficients (adaptive filter 28) that are completely different from the pulse response of the merge 6, it is considered unstable. As described above with reference to Figure 37, the coefficients of the adaptive filter 28 attempt to "analog" the 16 impulse response and subtract it from the output signal in an attempt to eliminate reflected echoes. However, if the adaptive filtering coefficient is too different from the impulse response, unnatural signals, such as distortion of speech or data signals, or even system noise may occur. System 10 instability can occur under the following two conditions: Echo cancellers 2086017 -39- 200417167 and 22 are in a closed loop system and are activated by a specific type of signal, resulting in a gain of system 10 greater than 1. , And (2) the echo canceller 20 is in an open loop system. Fig. 14 is a specific embodiment of a dynamic gain control method for monitoring the gain of the echo canceller 20, which can be implemented by a gain monitor 100 coupled to the adaptive filter 28 and the gain control 33. The dynamic gain control method of FIG. 14 ensures the stability of the echo cancellers 20 and 22 in the closed loop system. For example, if the right error number 46 is greater than Sin 3 8 (which should not occur in theory, but can occur in practice) ', then the gain of the echo canceller 20 is greater than or equal to one. If the same situation occurs in the echo canceller 22 (causing the gain of the echo canceller 22 to be greater than or equal to one), the entire loop gain of the closed loop system having the echo cancellers 20 and 22 may be greater than one, which may generate an unnatural signal , Known as the cymbal sound. Therefore, when the ratio of the power of the error signal 46 (Perr0r) to the power of Sin 38 (PSin) within a certain time window (see formulas 1 and 2 above) is greater than the adaptive limit, the method of FIG. 14 weakens the error signal 46 . In addition, the method of FIG. 14 resets the adaptive filter 28 when PerrQr is several times larger than Psin. Therefore, the method in FIG. 14 prevents the overall loop gain of the closed loop system from being greater than one at any time, ensuring the stability of the system 10. In addition, the method of FIG. 14 also accelerates the reconvergence of adaptive filters 28 due to sudden changes in merge characteristics. Accordingly, FIG. 14 illustrates a portion of block 218 of FIG. 9. That is, after detecting whether the near-talker signal exists in block 216 in FIG. 9, the flow proceeds to block 218, where the gain of the echo canceller 20 is monitored and selectively adjusted. Therefore, the process starts by determining the diamond 322, in which p p D ，, / / 、 T 疋 [error to PSin ratio (pern> r / pSin) is greater than the reset limit. If yes, the flow proceeds to block 33, 86017 • 40- 200417167, where the filter coefficients of the adaptive filter 28 are reset (in a specific embodiment, it is set to zero). Alternatively, the coefficients can be reset to any value. Therefore, resetting the limit can be used to determine whether Perroi * is much larger than Psin, and it is necessary to reset the adaptive wave filter 28 to prevent instability. Therefore, the reset limit can be any value, and is set to 8 in a specific embodiment. If Perr0r / PSinf is greater than the reset limit, the process continues to determine diamond 324, where it determines whether perr () r / PSin is greater than the gain limit. The gain limit is usually less than the heavy limit and is set to 1 in the specific embodiment. This gain limit is the limit used to start the gain reduction start. If PerrQr / Psin is greater than the gain limit of 5, the process proceeds to block 328, where the gain is adjusted using Aval, as shown in Equation 7 below. Equation 7: Gain == Aval * Gain Aval is usually less than 1 and attenuates the error signal 46. Therefore, in a specific embodiment, Aval is 0.9996. The flow enters the decision diamond 328, where it is determined whether the gain 小于 is less than the gain limit. If so, the flow proceeds to block 334, where the gain is set to a gain limit. This ensures that the gain does not fall below a predetermined level (0.5 in a specific embodiment). For example, it is usually not necessary to completely cut off the recitation path (ie, gain = 0), even under some abnormal solutions, such as calling for merging in an open loop circuit. The flow then proceeds to block 326. If the diamond 332 is determined to determine that the gain is not less than the gain limit, the flow proceeds to block 326, where the error signal 47 is calculated as shown in Equation 8 below. Formula 8: Error signal 47 = Gain * Error signal 46 If it is determined in the decision diamond 324 that pem) r / Psin is not greater than the gain limit, the process proceeds to determine the diamond 336, which determines whether the gain is less than 丨. If not, the flow proceeds to block 326, where the error signal is attenuated; however, if it is less than i, the flow 86017 -41- 200417167 flows to block 3 3 8, where the gain is adjusted as in the following formula 9: Formula 9 · · Gain = Beta * Gain beta is usually greater than 1 because the gain has previously been attenuated and it needs to be restored. Therefore, beta is 1.0004 in a specific embodiment. The flow then proceeds to a decision diamond 34o 'where it is determined whether the gain is greater than one. If yes, the flow advances to block 326, where the error signal 46 is attenuated. If not, the flow advances to block 342, where the gain is set to one. After block 342, the flow proceeds to block 326, where the error signal 46 is not attenuated 'because the error signal 47 is equal to the error signal 46 * 1 (because the gain has been set to 1 at block 342). Therefore, if PerrQr / Psin is greater than or equal to the reset limit, the filter coefficients of the adaptive filter 28 are reset. If Perr () r / Psin is less than the reset limit but greater than or equal to the gain limit, the error is attenuated by the gain value (for example, in block 326). However, if perrQr / Psin is also less than the gain limit value, then the error will not be attenuated (ie, error signal 47 = error signal 46). In turn, it can be understood how the flow of FIG. 14 helps to maintain the stability of the system 10. FIG. 15 is a specific embodiment of a filter coefficient monitoring method for monitoring the filter coefficient distribution status of the adaptive filter 28, which can monitor and control the filtering within the single το 30 and is coupled to the adaptive filter 28 The controller coefficient monitor 102 executes. The method of FIG. 15 ensures the stability of the echo canceller 20 in the open loop system. The monitoring method detects the adaptive filter 28 with a relatively balanced distribution state—the formation of a set of filter coefficients. Since the expected pulse response of the combined 16 'adaptive filter 2 and the balanced distribution of the 8 coefficients indicate that there is no merge, it indicates the possibility of an open loop condition. Therefore, by detecting the equilibrium distribution state of the coefficients of the adaptive filter 28, the filter coefficients are reset, and the echo canceller 20 is put into an alarm state for further monitoring. When the filter coefficients are reset repeatedly in a specific time window of 86017 -42- 200417167, it is assumed that the echo canceller 20 is in an open loop condition and the echo canceller 20 is bypassed. That is, only the adaptive merger adaptive filter 28 exists. In addition, the adaptive filter 28 in the open-loop system with continuous sinusoidal input through Rin and non-zero signal Sin (such as sine audio) can quickly diverge, thus increasing the need to detect the open-loop system. Accordingly, FIG. 15 illustrates a portion of block 228 of FIG. 9. That is, after the filter coefficients are backed up in the square block 226 of FIG. 9 (as described above), the flow proceeds to a block where the coefficients of the adaptive filter 28 are monitored. So the process starts at the block

344, where the filter coefficients of the adaptive filter 28 are divided into 8 bins. (B selects the number of filter coefficients / 16.) The flow proceeds to block 346, where b is determined

Maximum and minimum coefficient power of each meal level. That is, if the filter coefficient is divided into B bins, each bin will be related to one of the power values of the coefficients in the bin (for example, the average power of the coefficients in the bin). In block 346, select the B bins. The maximum power value and the minimum power value of B bins. The process continues to the decision diamond 348, which depends on whether the ratio of the maximum power value to the minimum power value (that is, maximum power / minimum power) is less than the alarm limit. If the filter is adjusted to the true union, the ratio of the maximum power to the minimum power should be much greater than 丨. On the other hand, if the ratio of the maximum power to the minimum power is close to the value, it clearly indicates that the comparator is not adapted to the true merge. The ratio is used as an alarm limit to signal no merge possibility. The selection of alarm limits is based on statistical analysis of the behavior of adaptive filters under various mergers. In a specific embodiment, the alarm limit is selected as 80, and the 'after comparison' process continues to block 35, where the filter coefficients of the adaptive filter 28 are reset to zero (or set to any other-or Multiple scheduled weights (86017 • 43-200417167 setting). Flow continues to block 352, where the alarm status is incremented. (The alarm status indicates how many times the filter coefficient has been reset in the current time period when the ratio of the maximum power to the minimum power is less than the alarm limit. It should be noted that the current time period is the same as J * L described above with reference to Figure 12, because When leaving blocks 3 and 10 of FIG. 12, the flow continues to block 228 of FIG. 9, which is illustrated in FIG. 15 and starts at block 344 of FIG. 15. That is, FIG. 15 is regarded as a background processing part, which is at most every J * L samples are entered, as shown in Figures 9 and 12.) After block 352, the process proceeds to a decision diamond 354, which determines whether the alarm state is equal to the bypass limit. If not, the echo canceller 20 will not be put into the bypass mode, so the adaptive wave filter 28 will be adjusted. However, if it is determined that the diamond 3 5 4 alarm state has reached the bypass limit, the flow proceeds to block 356, where the bypass mode is set to i, indicating that the open loop condition has been detected (that is, there is no merge), so the echo cancellation is bypassed The device 20 avoids adapting to non-existent merges. If in the decision diamond 348 determines that the ratio of the maximum power to the minimum power is not less than the alarm limit, the flow proceeds to block 358, where the alarm state is reset to zero. The flow enters the decision diamond 360, which determines whether the bypass mode is 1, and if yes, resets it to 0 in block 362. Therefore, the branch of 358 makes the merging 16 reconnectable, in which the adaptive filter 28 starts to adapt again. FIG. 5 is a part of the non-linear processor 32 of FIG. 2. As mentioned above, in addition to reducing or removing the residual echo, the 'non-linear processor 3 2 also attempts to save or match the background noise of the signal of the near-end talker, which provides improved communication quality. Generally, the non-linear processor 32 detects whether the residual echo is below a certain limit and replaces it with appropriate noise instead of silence to avoid the sudden disappearance of background noise on the telephone line. The sudden disappearance of this kind of scene noise can lead to the impression that the telephone company has been disconnected from 86017 -44- 200417167. One prior art method that is still in use uses synthetic background noise, however, 'this can lead to a destructive exchange between real background noise and synthetic background noise. For example, one prior art method that is still in use uses white noise as the appropriate noise. However, white noise is very different from natural background noise ’so it sounds destructive. An alternative solution currently available is to repeatedly output a pre-stored background noise signal to match the background noise. However, this method requires additional storage space, causing significant background noise to repeat, which is also destructive to communication. Therefore, 'a specific embodiment of the non-linear processor 32 is provided in FIG. 5 and FIGS. 16 to 19', which stores or matches the natural background noise in the echo canceller 20, so as to reduce the non-natural signal caused by the non-linear processing by reducing the return , Such as the destructive unnatural signals described above. The non-linear processor 32 uses the short-term level estimator 88 and the long-term signal level estimator 92 to find a reliable estimate of the true background noise signal level and adjust its limits (NLp_ON and NLPJ3FF limits' This is explained below). The short-term estimator produces a fast level estimate of the background noise signal at the beginning of the call. On the other hand, the goal of the adaptive feature of the long-term estimator is to reliably track the background noise signal level at any time. The decision to activate the non-linear processor 32 is based on the relative levels of the far-end signal, near-end signal, and background noise signal. When the background noise signal is significant, the non-linear processor 32 saves the original background noise signal by transmitting it through the echo canceller 20. When the background noise signal is low and the residual echo is audible, the non-linear processor 3 2 replaces the residual echo with a suitable noise signal whose level is a pair of dB lower than the estimated background noise signal level. The resulting suitable noise signal is also gradually mixed into the original 86017 -45- 200417167 background noise signal in order to minimize the conversion audibility. Therefore, the non-linear processor 32 saves natural background noise or matched background noise and minimal audible effects when needed. The preservation or matching of the natural background noise in the echo canceller 20 is performed in four basic steps: (1) estimating the level of the background noise signal, the far-end talker signal and the near-end talker signal; (2) determining the nonlinearity Limits of the processor 32; (3) if a non-linear processor 32 is needed, suitable noise is generated; and (4) if a non-linear processor 32 is needed, suitable noise is mixed into background noise. The non-linear processor 32 of FIG. 5 includes an adaptive background level estimator 96, which includes a short-term background level estimator 88, a background level estimator controller 90, a long-term background level estimator 92, and a background level configuration.接 器 94。 94. The background noise level estimation is performed by a short-term background level estimator 88 and a long-term background level estimator 92. When a call is turned on, the short-term background level estimator ⑽ provides an initial fast estimation, and the long-term background level estimator 92 gradually adjusts to the level of the background noise signal at any time. It should be noted that when the background noise level changes, the adaptation rate of the long-term background level estimator 92 to a higher noise level is slower than the adaptation rate to a lower noise level. Therefore, the estimators 88 and 92 are activated when the levels of the near-end and far-end talker signals are both below a predetermined limit. That is, if a value is available for long-term background level estimation, only the estimator 92 is used. Therefore, when the long-term background level estimator 92 is not yet available, the short-term background level estimator is "usually = used to start (that is, call start). (The levels of the near-end and far-end talker signals are determined by the near-end signal, respectively. Level estimator 70 and remote signal level estimation are determined.) ^ The limit for turning on the non-linear processor 32 (implemented by the non-linear processor on-off control 86017 • 46- 200417167 controller 76) is different from Turn off its limit (executed by non-linear processor shutdown controller 78). When the near-end talker signal is negligible and the far-end talker signal is enabled, the non-linear processor enable controller 76 is enabled (or enabled) ) Non-linear processor 32. When the near-end talker signal is relatively high or the background noise signal is significant, the non-linear processor closes the controller 78 to disable (or close) the non-linear location w 32 to eliminate residual echo and save The balance between the actual background noise is as follows ^ 3 When the $ Noise # is relatively high, the non-linear processor 32 is disabled, so that the background noise can pass through the echo canceller 20. In this case, it can be ignored Residual back ^ due to the mask effect is Masked by significant background noise signals. When the background noise signal is relatively low and is more audible due to the presence of residual echoes and quieter background noise signals, the non-linear processor 32 is activated. In both cases, 'by adapting The noise filter 28 achieves a better convergence depth, and the residual echo is small. When the non-linear processor 32 is started, suitable noise generation (by the suitable noise generator 86) and noise level matching (by the noise bit The quasi-matcher 82) minimizes audible "noise gating" (ie, transitioning from one background to another or from one background to silence) for feeling the conversation. Several types of suitable noise signals can be selected to be close to natural background noise signals. In addition, to smooth the transition, suitable noise usually replaces the actual background noise (by the output signal mixer 84), and the appropriate noise level is set to a pair of dB below the estimated background noise level. FIG. 16 illustrates a method for performing adaptive background level estimation according to a specific embodiment of the present invention. Generally, the level of the background noise signal can be estimated only when the following three conditions are met (corresponding to the decision diamonds 400, 402, and 404 in Figure 16), 86017 -47- 200417167 ⑴ No near-end talker signal '(2) There are no far-end talker signals (that is, no residual echo) and (3) the above two conditions have been met for some time. First, the decision diamond 400 determines whether the near-end talker signal level (Pem) r) is below the error power limit. The error function value is the limit for deciding whether to consider an error signal as a background noise signal or a near-end talker signal. In a specific embodiment, the error limit is _39 dBmG. This check reduces the possibility of mixing near-end talker signals with scene noise signals because the background energy estimates described below cannot include near-end talker signals. If PerrQr is less than the error limit, the process proceeds to decision diamond 402 where a second condition is checked. In decision diamond 40, it is determined whether or not the remote talker signal level (PRin) is less than the Rin limit in order to eliminate the residual echo in the rabbit scene level estimation. The Rin limit is defined as the level of the Rin signal sufficient to produce a significant residual echo before the non-linear processor. In a specific embodiment, the Rin limit is -27 dBmO. If PRin is less than the Rin limit, the process proceeds to a decision diamond 404, where it is determined whether the two conditions before a specific time window (ie, background reverberation time) have been met. That is, if the background reverb timer = 0, then the two conditions before the special time window (defined by the background reverb time) have been met, and the process proceeds to block 408. The background reverberation time is used to ensure that the far-end and near-end talker signals are no longer within a specific time window. In a specific embodiment, the background reverberation time is 160 samples, or 20 ms, assuming a sampling rate of 8 kHz. If it is determined that the diamond 400 PerrQr is not less than the error limit or if the diamond 402 PRin is not less than the Rin limit, the process proceeds to block 406, where the background reverb timer is set to a predetermined value, such as the background reverb time described above. Then go to point C after the mud journey. (It should be noted that at point C, the flow continues to Figure 丨 8, which will be further explained below.) If the decision is made to determine the diamond shape 40, the background reverb timer 86017 -48- 200417167 is not 0, then the background reverb timer is at Block 41 is decremented, and the flow enters point C. However, when the three conditions of the decision diamond 400, 402, and 404 are satisfied, the flow proceeds to block 408, where the background level (Pbackgn) und is adjusted to meet the needs (Pnew_background) 'is determined by the subsequent steps. (It should be noted that Pnew_backgr_d will be calculated and explained with reference to block 426 in FIG. 17, so in the substitution of block 408, 'Pnew-background may have any appropriate initial value, such as an initial value representing a suitable noise level.) Smooths the transition from one signal level to another in suitable noise level matching, and each sample is adjusted. Therefore, the adaptation is performed as shown in Equation 10 below. Equation 10Pbackground (n) = ((R-l) pbackgr () und (n)) +

Pnew_background] / R In Equation 10, Pbackgr () und (n) is the estimated background power level at time η; Pnew_background is the new background power level adjusted (determined in the fourth step); R is the control The adjustment rate factor is set to FAST_RATE or SLOW_RATE. (It should be noted that R can be set in block 428 in FIG. 17 or block 480, 472, or 476 in FIG. 19, which will be described in detail below. Also, it should be noted that in the specific embodiment, the 'FAST-RATE adjustment rate is set to 29 and SLOW A RATE is set to 211.) After block 408, the power level estimation of the background noise signal starts, which includes 3 main steps. The first step in estimating the background noise signal power level is to calculate the background power level in a window. Therefore, the flow proceeds to block 412, in which the power of the scene (PwindGW_baekgr () und) is calculated as shown in Equation 11 below. ~ 86017 49- 200417167 Formula 11:

) 1 ^^ Size-X window background =; ~~~ 7 (error singal 46) 2 ^ size ^ v & / Error

y \ 1 1 Φ, P ^ T rwindQW-backgr () und is the window background power level estimation, the error signal 46 is the difference between sin 39 and the estimated signal data output by the adder 34 in Fig. 2, w- size is the average window size. In a specific embodiment, w_size is 64 samples. The flow then proceeds to block 414, where the background sample counter is incremented. The second step involves finding the smallest Pwindow_background, W_COimt in a specific number of time windows. (In a specific embodiment, w-count is 128 samples; however, in alternative embodiments, according to the number of time windows w-COUIlt required to calculate the minimum Pwindow_background can be any value.) Therefore, the calculation of block 418 ( As shown in the following formula 12) It is executed once every w-size samples. To perform the second step, the flow proceeds to a decision diamond 416, where it is determined whether the background sample counter is w-size. If not, the process enters point c (in Figure 18). If yes, the flow proceeds to block 418, where the minimum power of the windowed background is determined as shown in the following formula. Equation 12Pmin_window_baekg_d = MIN (Pold_min_wind〇w_backgr_d:

Pwindowbackground) Therefore, Pmin_window_background is determined by choosing the smallest value between the old minimum power (the minimum power determined in the previous iteration of block 418) and the Pwindow baekground determined by block 412. The flow then proceeds to block 420 'where Pwindow_background is reset to zero. Flow proceeds to block 422, where the background sample counter is reset to zero and the window counter is incremented. The flow then proceeds to point A, which continues in Figure I7 (starting with decision diamond 424). The third step λ of the adaptive background level estimation is to determine the pnew baekgr_d for the background level adjustment described with reference to block -50- 86017 200417167 408, and to determine the adaptation rate used in block 408. There are two different methods for determining Pnew backgr based on whether it is the first time (> und. Therefore, in decision diamond 424, it is determined whether it is an initial estimate (indicating that no long-term data is available, such as at the beginning of a call). If so, the flow proceeds to block 426 , Where Pnew_background is set to Pwindow_background calculated in the first step. The process then proceeds to block 428, where the adaptation rate R is set to FAST_RATE. However, the 'right' decision diamond 424 determines this non-initial estimate (indicating that Pnew_background is available (Because long-term data are available, such as N previous samples), the process enters the decision diamond 430. It should be noted that if it is not the initial estimation, the method of Pjiew_ background is performed every W_COUnt windows. Therefore, in the decision block 430, it is determined whether the window counter reaches ~ -cunt. If not, the flow enters point C (in Fig. 18). However, if it is, the flow enters block 432 'where the difference is Pnew-background. The flow then proceeds to block 434 where the decision疋 Adapt rate R. (The decision of PneW-baekgr () und and r will be explained in more detail with reference to FIG. 19). 436, where the window counter is reset to 0, and then proceed to block 438 'where Pmin_wind〇W-backgrund is reset to 0. Then the process enters point C. Figure 19 is determined when Pnew_background is available? _Add ... And r. The method in Figure 19 prevents pneW-backgr () und from having a large jump from a lower level to a higher level, but when the change is from a higher level to a lower level, this change is faster Therefore, such restrictions are not allowed. Therefore, in a specific embodiment, Pnew_background is limited to not more than twice Pbaekg_nd. If the adjustment is from a higher level to a lower level, the method of FIG. 19 also sets a faster adjustment. Rate (FAST-RATE). If the adjustment is from a lower level to a higher level, the rate is set to a rate lower than 86017 -51 · 200417167 忮 (SLOW-RATE). The use of different rates is based on the background noise level, which is usually changed from low. It sounds better when changing from high to slow, but it changes quickly from high to low. Figure 19 illustrates a part of blocks 432 and 434 of Figure 17. The process begins with the decision of Lingmu 466 'which depends on pmin_wind〇w-backgr〇und Whether it is several times larger than the reed, that is, Prni n—window_background > Constant * Pbackground ”, one of which is a constant in the specific embodiment. If so, the flow proceeds to block 4 7 8 where Pnew_background * is set to“ (factor * Pmin_wind 〇w_background) + suitable noise level. " In a specific embodiment, the constant in block 478 is 2 (where 2 corresponds to 0.5 in the previous sentence). Flow then proceeds to block 480, where the adaptation rate is set to SLOW-RATE. The flow then proceeds to block 436 in FIG. Right in the shape of love 466Pmin win (j〇w_background is not greater than "constant P backgr.und" 'then the process enters the decision diamond 468, which determines whether Pmin_window-background is greater than Pbackground. If yes, the process enters block 474' where Pnew_background is set A background 0 process for Pmin_ window then proceeds to block 476, where the adaptation rate R is set to SLOW_RATE. Then the process proceeds to block 436 in Figure 17. However, if Pmin_wind is determined in the decision diamond 468. w_background is not greater than Pbackground ', then the process enters block 470, where Pnewbackground is set to "Pmin_window_background + suitable noise level." The process then proceeds to block 472, where the adaptation rate R is set to FAST-RATE, and then to block 436 in Figure 17. Therefore, it should be noted that when Pbackground or Pmin_ window— When the background is exactly 0 ', in order to prevent Pnew_backgrund from being silent, a suitable noise level (CNL) (in blocks 478 and 470) should be added. For example, in a specific embodiment 86017 -52- 200417167 CNL Set to -66 dBmO. Alternatively, CNL can range from -60 to · 7 Within the range of 2 dBmO. Similarly, although the process of FIG. 19 is performed using power level estimation, the same process can be completed using numerical estimation. FIG. 18 is a specific embodiment of the present invention using all the level estimation controls obtained above Method of Non-Linear Processor. That is, FIG. 18 illustrates a portion of block 230 of FIG. 9 in which non-linear processing is performed. In FIG. 18, the flow begins at point C (which may come from, for example, block 406, block 410, or decision of FIG. Diamond 416, or block 438 from Figure 17.) The flow continues from point C to decision diamond 440, where it determines whether PerrQr is greater than the non-linear processor off (NLP_OFF) limit. If so, the flow proceeds to block 452, where NLP_OFF (set Turn off the non-linear processor 32), and then proceed to block 454, where the noise ramp factor is reset to a predetermined value. The noise ramp factor is used to smooth the transition of the signal level from low to high. (The flow after block 454 enters FIG. 9 Block 232.) If it is determined in decision diamond 440 that Pern) r is not greater than the NLP_OFF limit, the process proceeds to decision diamond 442, where it is determined whether PbaekgrQund is greater than the background limit. If so, the flow proceeds to block 452, where the non-linear processor 32 is turned off, and then proceeds to block 454. Therefore, the non-linear processor 32 is turned off when Perror is greater than the NLP_OFF limit or when Pbackground is greater than the background limit. In a specific embodiment, the NLP_OFFP value is set to -27 dBmO and the background limit is -39 dBm0. If the decision diamond 442 determines that Pbaekgn) und is not greater than the background limit, the process enters the decision diamond 444, where the decision Pern) r Whether it is less than the non-linear processor start (NLP_ON) limit. If so, the process proceeds to decision diamond 446, which determines whether AVGPRin is greater than the PRin limit. If yes, the flow proceeds to block 448, where NLP_ON is set (indicating that the non-linear processor 32 is started). Therefore, the non-linear processor 32 is activated when Perror -53- 86017 200417167 is less than the NLP_ON limit or when AVG PRin is greater than the PRin limit. The condition that the AVG PRin is greater than the PRin limit ensures that the non-linear processor 32 is activated only when needed (because there will be a significant echo only when the far-end talker signal is relatively strong). On the other hand, the condition that Perr0r is less than the NLP_ON limit further ensures that the residual echo must be small and that the near-end talker signal is not mistakenly considered as the residual echo removed. Therefore, in a specific embodiment, the PRin limit is set to _36 dBmO and the NLP__ON limit is set to -42 dBmO. However, in alternative embodiments, they can be set to any appropriate value. It should be noted that in the above specific embodiment, the difference between the NLP_OFF limit and the NLP_ON limit (-15 dBmO in a specific embodiment) is the "dead zone" of the non-linear processor 32, which helps to avoid NLP_ON And NLP_OFF fast exchange. If it is determined that Perror is not less than the NLP-ON limit (located in decision diamond 444) or AVG PRin is not greater than the pRin limit (located in decision diamond 446), the process proceeds to decision diamond 450, which determines whether to set NLP-ON (that is, non-linear Whether the processor 32 is started). If NLP_ON is not set, the process proceeds to block 232 in FIG. 9; however, if it is set (or after leaving block 448), the process enters a decision diamond 456, which determines whether to enable appropriate noise. If not, the flow proceeds to block 232 in FIG. 9, but if it is activated, the flow proceeds to block 458 where a suitable noise is generated. After block 458, the flow proceeds to block 460 where the appropriate noise level is determined 'and then proceeds to block 462 where the appropriate noise is mixed with the background noise. The flow then proceeds to block 464, where the noise ramp factor is adjusted, and then to block 232 of FIG. Therefore, a suitable noise signal is generated when the non-linear processor 32 is activated. 86017 -54- 200417167 White noise is usually not a better choice for suitable noise, because it is too far from the background noise of daily life #No. Some embodiments of the present invention therefore use pink noise, brown noise, or Hoth noise as suitable noise. For example, in one embodiment, 'pink noise' is selected because of its low computational complexity. As shown in Equation 13 below, pink-like noise can be generated by continuously achieving two pseudo-random variables X of the equilibrium distribution (for example, in block 458). Formula 13: Ypink (n) = C, X (n) + C2 * X (nl) In the above formula 13, X (n) is a pseudo-random variable X (η) < 1) generated by time n, (^ and ^ Is used to modify the mixture of two random samples and the constant of the Ypink value. Therefore, Ypink (n) is a pink noise sample generated at time n. The two constants Ci and C2 are selected to ensure the pink noise signal The average power level is about 2 dB below ^ background. For example, in a specific embodiment, Cl and C2 are selected as 0.75 and 1. Therefore, in a specific embodiment, the noise matching level is suitable. The estimated background noise level ranges from 0 to 4. The appropriate noise Ypink generated in this specific embodiment is then mixed with the background noise 'as shown in Equation 14 below (see also block 462 in Figure 18). Equation 14 Sout (n) = a (n) * (error signal 46)-(1-α (η)) * Α *

Ypink (n) In Equation 14 above, A is the value of the background noise level to be matched (corresponding to block 460). For example, in a specific embodiment, the square root. In the alternative embodiment, if Pbaekgr. —Represent ‘A — pbaCkground as a value, not power. In Equation 14, α (η) is the noise ramp factor at time η (where 0 $ α < 1), which provides a smooth transition from one bit rate to another at the beginning of the non-linear processor 32, Sout (n ) Is the final output of the non-linear processor 32 at time η 86017 -55- 200417167 (that is, Sout (n) is Sout 42 of Fig. 2). The noise slope factor is calculated for each sample using Equation 15 (adjusted at block 464). Formula 15: a (n) = b * α (η -1) In formula 15, 'b is the slope constant', and the choice is less than 1. In a specific embodiment, it is about 0.9986, which is itself attenuated in about 500 ms because 0.99865ί) () = 0.496. During this ramp method, Sout⑻ starts from error signal 46 (which is Sin 39-error estimation signal 48 of Figure 2) and gradually changes to a * Ypink (n) 'If the ramp method continues, α (η) changes from 1 to 〇. The ramp can be applied to the start and offset of the non-linear processor 32. However, in one embodiment the ramp is applied only to the beginning of the non-linear processor 32. The reason is that when the non-linear processor 32 is turned off, it usually detects a significant level of near-end talker signal, and it may not be necessary to gradually switch back from suitable noise (a pink noise signal in a specific embodiment) to Near-end talker signal. However, alternative embodiments may apply this ramp when the non-linear processor 32 is turned on and off. Figure 7 is part of the monitoring and control unit 30, whose function is to eliminate pure delay. As described above, pure delay estimation is used to reduce the number of taps of the adaptive filter 28, thereby achieving faster and deeper convergence with fewer calculations. That is, a part of the monitoring and control unit 30 illustrated in FIG. 7 and the flowcharts in FIGS. 20 to 24 can be used to detect pure delay and locate the sparse window (block 211 in FIG. 9). A specific embodiment detects pure delay and locates a filter window (sparse window) with the correct size in order to reduce the length of the adaptive filter 28 (i.e., the number of taps). Therefore, Fig. 7 will be explained with reference to the flow charts of Figs. Figures 7 and 20 to 24 provide a specific embodiment for realizing the pure delay (that is, T1 in Figure 37) of the echo signal (for sparse window positioning in the echo canceller 20) estimates 86017 -56- 200417167. Perform pure delay estimation, as explained in detail below. You Yishan Meng is calculated by replacing the full window adaptive chirps, wave filters, and cores with the correct range of narrow window adaptive filters. cost. That is, T uses a full window adaptive attenuator that covers the entire impulse response of Figure 37, which is large enough to cover T1 and T4 + T2, while using a smaller window (sparse window), which excludes the pure delay part and captures T4 + Ding 2 positioning, there will be a significant response in this part,. Also, 'pure delay estimation increases the convergence speed and depth of the adaptive filter 28 by using a shorter length adaptive filter. Similarly, pure delay estimation can be used to dynamically monitor pure delays that change the echo (for example, in a phone call) and adjust the adaptive filter window (for example, the sparse window) accordingly. Specific embodiments to be described herein may include passive methods (such as sub-rate filter adaptation using only dialog signals) and active methods (such as injecting short, narrowband very low-level quasi-noise pulses at the beginning of a call, and The pure delay performs sub-rate adaptation, which starts with silence in both directions, where silence usually lasting 300 ms is sufficient to inject low-level detection signals and determine pure delay). The specific embodiments described herein also include two situations for handling pure delays. The first situation is about the beginning of a telephone call, in which the Quality of Service (QoS) principle needs to reduce the echo quickly. The second situation is about changing the echo path in the middle of a phone call. In general, the sparse window (and associated pure delay) does not change throughout the duration of a phone call. However, the pure delay in some calls (especially those where "Call Forwarding" or "Conference Call" is activated) can vary significantly. Therefore, the various embodiments described herein support dynamics of pure delay, which correspond to changes in the sparse window up to once per second. It should be noted that the specific embodiment described herein may use the exclusive (ie, non-standard) signaling provided by the control signal 17 to determine whether the phone is on-hook or picked up 9 to determine the start or end of the call. The specific embodiments of Figs. 7 and 20 to 24 may use a sub-rate adaptation method that provides a valid estimate of pure delay calculations. However, alternative embodiments may not use the sub-rate method. Similarly, in a specific embodiment, in order to deal with the inherently variable estimation of pure delay, the raw measurement result of pure delay can be filtered non-linearly before it returns to adaptive filter 28 (that is, using a decision or qualification method, examples of which will be referred to Figure 23 illustrates). The above-mentioned sub-rate method may use a normalized least mean square (NLMS) adaptive filter (for the adaptive filter 122 of FIG. 7). However, the adaptive filter 122 is not limited to this type of adaptive filtering, for example, it can adapt to PNLMS, RLS, or other adaptive filters. It should be noted that NLMS adaptive filtering algorithms are usually simple and have acceptable convergence characteristics. Other adaptive filter algorithms require more calculations. Proportionate Normalized LMS (PNLMS) algorithms provide a tangible improvement in convergence characteristics at appropriate computational costs. Recursive Least Squares (RLS) adaptive algorithms are usually significantly faster (but computational costs are also significantly higher). However, it is sensitive to digital errors and digital instability. Other adaptive filters (such as subbands, affine, and their variations) are more attractive from the point of view of convergence characteristics, although they are computationally more demanding than NLMS. However, the specific embodiments described herein are not limited to the use of NLMS adaptive filtering. Both primary rate adaptive filters and sub-rate adaptive filters can be based on other types of adaptive filtering solutions. 86017 -58- 200417167 The delay estimation can be controlled by mechanisms such as short-term sub-rate signal power estimation and sub-rate near-end talker signal detection to prevent inherently unreliable (noisy or near-end calls Measurement) and therefore the divergence of the sub-rate adaptive filter 122. It should be noted that in the above description with reference to the adaptive filter 28, in order to avoid the termination of the error coefficient when detecting S ge η, the same principle can be applied to determine the adaptive filter used in the pure delay. In addition to the filter length, pure delay estimation can be used to handle other situations, such as when the far-end echo canceller is turned off, when the call is switched from the local to a long distance (for example, by the call transfer function, call transmission function, etc.), when the conference Call operations have calls / callees spread over a large geographic area, and so on. Figure 7 illustrates the monitoring and control unit 3G # _min used to provide an estimated delay of 13 in the form of a 10,000-block diagram. In the specific embodiment of the adaptive waver 122, it uses sub-rate processing, which is a sub-rate adaptive filter) to provide a short-term estimate of the band-pass impulse response on a continuous basis during the phone call performance time. The pure delay of the pulse response is a continuous filtering using a qualification method or a decision method (for example, Fig. 23), which may be a non-linear wave filter as described above. This waver provides quick decision of pure delay at the beginning of the call, and provides pure delay adjustment or new pure delay selection in the middle of the call, but the new pure delay value is related to the verification of the new value. Minimum, in the middle of a call, switch from one pure "to another" which can be properly verified based on pure delay measurement ... In a specific embodiment, the verification provides a conservation machine to change the pure delay in the middle of the call. Three or more sub-rate pulses return to the measurement result of the maximum position (86017 -59- 200417167). In an alternative version, as explained with reference to Fig. 24, the echo canceller 20 may be operated in a monitor mode. In this mode, the system of Fig. 7 is activated only at the beginning of a telephone call (that is, an estimated pure delay), and then if it meets a specific condition, it enters a standby state. In the standby state, the ERLE estimator continuously checks the ERLE of the corresponding adaptive filter 28 with the limit value. If the ERLE drops below the limit value and maintains a predetermined duration ', the system of Figure 7 returns to the dynamic mode and continues to estimate the pure delay. The mud course of FIG. 20 starts with the decision diamond 482, in which it is decided whether the pure delay estimation option 启动 is enabled. It should be noted that this option corresponds to the setting programmed into the echo canceller 20. In this case, the decision whether or not to activate the option need not be done on a per sample basis' as shown in FIG. 20. In an alternative embodiment, the decision to decide diamond 482 may be made at the beginning of a telephone call. However, only the pure delay estimation option start flow will enter the decision diamond 483. If it is not started (whether it is decided at the beginning of the call or on a per sample basis), the process proceeds to block 213 in FIG. 9 because pure delay estimation will not be performed. The decision diamond 483 decides whether to enable selective serialization of call start. Adapting to the pure delay estimation option, the tandem option can also be programmed into the echo canceller 20 to check at the beginning of the phone call instead of each sample, as shown in Figure 20. If the selective serialization is not started, the flow proceeds to the decision 'diamond 484' of FIG. 21. However, if selective serialization is initiated, the process proceeds to decision diamond 497. In short, if the selective serialization is not activated, the flow of FIG. 20 is not required. Similarly, if the pure delay estimation option is not enabled, the processes of Figures 20 and 21 are not required. Therefore, the echo canceller 20 can be operated in various ways, depending on the selected setting and option 0 86017 -60- 200417167. It should also be noted that at the beginning of each telephone call, a number of variables can be initialized for use in the processes of Figs. For example, in the '-specific embodiment, the tandem bypass flag is set to FALSE, the pure delay sample counter is reset, and the tandem cable is reset' The ERLE counter is reset. These variables will be illustrated throughout the process of Figures 20-24. Same as m-some values are programmable or S1 line type into the echo canceller 20. For example, the measurement period_ is initialized to one at the beginning of each call: fixed value or fixed line to echo canceller_. It should be noted that the other variables described throughout the description can be initialized or fixed or programmed (permanently or not) into the echo canceller 20 at the beginning of each call. On the right, the decision diamond 483 decides to start the selective serialization of the call, and the flow enters the decision diamond 497. Selective stringing allows pure delay to be estimated at the beginning of a call. Because there is usually no conversation at call-on # ', the serial signal can be injected into Rin 43 to generate Rout 40 (see the serial signal 41 in Figure 2, which can be added by adder 36 / main input 1 ^ 1143). That is, if the appropriate 1 ^ 1143 energy is used, it is impossible to determine the pure delay, so the injection of the serial signal 41 can be used to determine the pure delay estimation. Generally, the tandem signal 41 is a short burst of lower energy injected before a telephone call begins a conversation. That is, the tandem signal 41 is generally smaller than the injection limit, which in a specific embodiment is in the range of -30 dBmO to -55 dBmO. Therefore, if the selective serialization is started, the flow enters the decision diamond 49 ?, which determines whether the serial bypass flag is TRUE. If so, the flow enters the decision diamond 484 of Fig. 21, bypasses the series together and continues the pure delay estimation of Fig. 21, as explained below. If the decision diamond 497 is determined and the tandem bypass flag is not set to TRUE, the mudway enters the decision diamond 499, where it is determined whether the tandem index is less than or equal to 86017 • 61- 200417167 2. The _column index ensures that the tandem signal (if used) is injected only at the beginning of the call. As mentioned above, the serial index can be reset at the beginning of the call, so the first time the decision diamond 499 is reached, the serial index should be less than or equal to 1 (because it was initially reset to zero). As will be explained below, the serial index will be incremented to one (for example, in block 505 of FIG. 21) after the first measurement period (300 milliseconds in a specific fact example). This still provides injection of the serial signal because the serial index is still less than or equal to one. However, after the subsequent measurement period, the serial index will increase to two (for example, in box 505 in FIG. 21). From the point of the decision diamond 499, the flow enters the decision diamond 484 in FIG. 21, and it is no longer possible to inject the serial signal 41. Because a serial index of 2 indicates that it is no longer considered the beginning of a call. It is often undesirable to inject a serial signal at a time other than the start of the call because it is audible to the parties to the call. If the diamond is being determined and the serial index is less than or equal to one, the flow proceeds to block 489, which indicates that it is still considered to be the start of the call. In block 4, calculate the long-term power of Sm (PSin) and Rin (pRin) (which can be done using the above formulas 1, 3 and 4). The process then proceeds to decision diamond 490, which determines whether Psin is less than

Psin limit and whether PRin is less than a PRin limit. The first check (whether ρ ^ η is less than the PSin limit) ensures that there is no near-end talker signal gjgen. In a specific embodiment, the PSin limit is _50 dBmO. The second check (whether PRin is less than the pRin limit) ensures that there is no far-end talker signal. In a specific embodiment, the limit is -50 dBmO. If both conditions are met, the flow proceeds to block 492, which indicates that the conversation has not yet started and serial signals can be injected. Therefore, the serial signal is injected into the block 492 (for example, the serial signal 41 of FIG. 2) or the injection is continued (if this is the second time through the block 492). However, if the diamond 490 is determined, and both conditions 86017 -62- 200417167 are not met, the flow proceeds to block 495, where the serial signal flag is set to TRUE. That is, once psin or pRin exceeds their respective limits, they are bypassed regardless of the serial index (located in the decision diamond 497), thereby preventing the serial signal from being injected into Tian Xinhu through the king. After blocks 495 and 492, the flow proceeds to the decision diamond 484 of the figure. FIG. 21 shows a specific embodiment for performing pure delay estimation. The flow of Fig. 21 uses sub-rate processing so that only every D samples enter the flow, where D corresponds to one of the ten samplers 10 and 11 of Fig. 7. For example, in a specific embodiment, D is 8, in which only every eighth sample of Rin44 and Sin38 is processed. However, in alternative embodiments, D may be any value (including 丨, which indicates that sub-rate processing is not used because each sample is processed). Therefore, every 13th sample is considered a sub-rate sample. The pure delay sample counter is used to keep track of incoming samples of Rin 44 & sin 38 in order to capture every D-th sample. Normally, the pure delay sample counter is incremented after each sample and reset every Dth sample. The pure delay sample counter can also be reset at the beginning of each telephone call, as described above. Similarly, the delay sample counter can be shared with the sample counters of other processes described herein, or it can be a specific counter used only to estimate pure delay. In the specific embodiment of determining the diamond 484 'to determine whether the pure delay sample counter is equal to the attention, it should be noted that the pure delay sample counter is reset (that is, set to zero). However, in an alternative embodiment, the 'pure delay sample counter may be initialized to 1 and checked with D instead of d-1. Similarly, the other specific embodiment may initialize the pure delay sample counter to D or D_1 and decrement it until it reaches 1 or 0, respectively. Therefore, various embodiments of the decimal filter and the ten-to-one sampler can be used for the decimal filter 86017 -63- 200417167 of Fig. 7 and the ten-to-one sampler 106 and 110. It should be noted that the output of one out of ten samples is a sub-rate sample of Rin 44, which can be called RhSR, and the output of ten out of one sampler 110 is a sub-rate sample of Sin 38, which can be called SinSR. In determining diamond 484, if It is determined that the pure delay sample counter has not yet arrived.] 0-1 ', the flow proceeds to block 502, where the pure delay sample counter is incremented by one, and the flow private enters block 213 in FIG. However, if the pure delay sample counter has reached D-1, the flow proceeds from decision diamond 484 to block 491, indicating that the sub-rate sample has been reached. The pure delay sample counter is reset as described above in block 491 for detecting the next sub-rate sample.

The flow proceeds to block 485, where the power of the sub-rate Rin (pRinSR), the power of the sub-rate Sin (PsinSR), and the sub-rate near-end talker detection flag (si * -near-end-detect-flag) are determined. For example, the following formula can be used to determine pRinSR, PsinSR, and P error SR (k). Equation 16PRinSR (k) = (l-a) PRinSR (k -1) + αRinSR2 (k); Equation 17: PSinSR (k) = (l- α) PsinSR (k -i) + α SinSR2 (k); Formula 18 ·· PerrorSR (k) = (i_a) .perr () rSR (k_l) + a .errorSR2 (k) Note that in the above formulas (Equations 16 to 18), k is the signal subrate The number of samples, for example, let SinSR (k) = Sin (k · D). Equation 18 corresponds to the sub-rate error, wrong u Wu SR 'It corresponds to the difference between SinSR and the sub-rate echo estimate' y (k), which is determined by the sub-rate adaptive filter 122 of FIG. 7, which will be described below with reference to block 494 . Therefore, err0rSR (k) and Perr (> rSR (k) will be described in detail below. Similarly, in a specific embodiment of the above formula, a is set to 1/280, which corresponds to a human found in a telephone channel. For speech statistics, it should be noted that 1/280 is also 86017 -64- 200417167, which corresponds to a sliding window of about 70 milliseconds for the average filter bandwidth. However, different specific embodiments may use different Aval values. (Note that the above sub-rate power The calculation can be performed by the power estimator 120 and the power estimator 118 in FIG. 7.) The decision of sr_near_end_detect_flag can be completed in the same way as the detection of the near end # described above with reference to FIG. Η. Therefore, perr () The minimum values of rSR (k) and pSinSR (k) are compared with the NESD sub-rate limit (NESD_SR_threshold) to determine whether there is a near-end talker signal (Sgen). (It should be noted that this can be done by using Figure 7 The near-end cymbal detector 114 is executed.) The right one determines that sr_near_end detect_flag is correct and set it to TRUE. This flag is used to bypass the filter coefficient update of the sub-rate filter 122, because if it exists Near-end talker signal, as described above, Sin 38 It is no longer a representative of pure residual echo but a mixture of both Sgen & residual echo. Therefore, as explained above with reference to adaptive filter 28, the 'subrate / filter 122 should only be adapted when SinSR includes only subrate echoes (Ie when the near-end talker signal is not present.) Also, as explained above with reference to the adaptive filter 28, the sub-rate filter 122 should be adapted when pRinSR is high enough to prevent adaptation to channel noise. It should be noted as above As explained with reference to the adaptive filter 28, the near-end talker ID can be detected in single-dial and dual-talk situations. That is, using the above algorithm, Sgen can be used in the presence of only one near-end talker or near-end and far-end. It should also be noted that other methods can be used to determine whether the near-end talker exists. For example, a specific embodiment can use a Geiger algorithm, which is well-known for detecting near-end calls. After the block 485, the process enters the decision diamond 486, which determines whether pRin⑽ is greater than the minimum power limit of the sub-rate Rin. If not, the process enters 86017 -65- 20041716 in Figure 9 7 Block 213 bypasses the update of the sub-rate adaptive filter 122. As described above, this prevents the sub-rate adaptive filter 122 from adapting to channel noise. The minimum power limit setting of the sub-rate Rin in a specific embodiment is set It is -45 dBmO. If the minimum power limit is met, the process enters a decision diamond 487, which determines whether sr_near_end_detect_flag is FALSE. If the minimum limit is not met, the process enters block 213 in Figure 9, as described above, because there is The near-end talker signal thus bypasses the update of the sub-rate adaptive filter 122. If sr_near_end_detect_flag is FALSE, the flow proceeds to block 494, which indicates that Prusr is sufficient and there is no near-end talker signal. In block 494, the sub-rate echo estimate y (k) is calculated, and then in block 496, the coefficients of the sub-rate adaptive filter 122 are updated. In a specific embodiment, a modified NLMS algorithm (the modification is used in the sub-rate method) can be used to calculate y (k) and update coefficients. Formula 19: y (k) = XT (k) · H (k) The above formula 19 represents the filtering of the input signal X, where X (k) = [x (k), x (k -1), ..., x ( k-N +1) is the input signal vector (at subrate D) that extends over the duration of the FIR filter range. Therefore, x (n) = RinSR (n). Similarly, in Equation 19, H (k) is the filter coefficient vector (for sub-rate sampling) for the k-th iteration, where: Equation 20: H (k) = [h〇 (k), hi (k ), ..., hN-Kk)] 1 Formula 21: H (k +1) = H (k) + step-size · errorSR (k) · X (k) The above formula 21 represents the filter coefficients according to the NLMS algorithm Update the formula, where the NLMS sub-rate step_size can be expressed as follows. Formula 22: step-size = β / [γ + PRinSR (k)] 86017 -66- 200417167 In Formula 22, the ^ adaptation constant, which is the "protection" period, ensures that when pRinSR (k) temporarily becomes small, the adjustment formula is The update period of X is not too large, and the input signal power when sampling at the sub-rate (see Equation 16). Equation 23: errorSR (k) = SinSR (k)-y (k) (adjust at sub-rate Error) In the above formula, RinSR corresponds to the filtered and ten-sampled remote signals (which corresponds to the output of the ten-sampled sampler 106 in Figure 7), while RinSR corresponds to the filtered and ten-sampled echo signals (located in The output of one of the ten samplers 10 in Figure 7). It should be noted that when Sgeri does not exist, Sin 38 only includes the residual echo, so the output of the one of ten samplers 110 is used as a filter and ten Select a sampled echo signal. The variable H (explained with reference to Fig. 19) corresponds to a column vector representing the coefficient estimates of the sub-rate adaptive filter 122, and "τ" after η indicates a non-vector transposition. The signal y represents the SinSR estimate provided by the adaptive filter 122. The error SR is the difference between SinSR and y. Similarly, in a specific embodiment of the above formula, β is set to 2 · 9 * 2 · 5, and α is 1/128. In a specific embodiment, γ is set to a small value (relative to pRinSR⑻). For example, if PRinSR (k) is represented by a 16-bit fraction, y is usually k · 2-n, where k is a small integer. The flow then proceeds to a decision diamond 498, where it is determined whether n is equal to N, where in the current specific embodiment, N corresponds to the duration of a single measurement cycle. In a specific embodiment, N corresponds to 300 ms, and therefore corresponds to 300 sub-rate samples (assuming D = 8). For example, if a signal (e.g. Rin 44 and 8111 38) is sampled at an 8 kHz rate, then samples are received every 125 milliseconds. In the current specific embodiment, d is 8 ', so each D-th sample corresponds to 8 * 125 microseconds, which is equal to 1 millisecond. Therefore, every N sub-rate samples, the flow enters blocks 503, 500, and 501, where N is 300 in the current specific embodiment of 86017-67-200417167, so that 300 * 1 millisecond is equal to 300 milliseconds. Therefore, 'N can be defined as a predetermined number of time windows or sub-rate samples with a predetermined duration, which must be determined before the estimated delay processing at blocks 500 and 501. The value of N can be programmed or fixed into the echo canceller 20 and can be any value, depending on the frequency required to calculate the new estimated delay value. It should be noted that N corresponds to the convergence time (ie, short-term convergence time) used for the sub-rate adaptive filter 122. For example, if a window of 1024 samples (which corresponds to 1024 * 125 microseconds in the current specific embodiment, which is equal to a window size of 128 microseconds, assuming a basic sampling rate of 8] ^ 1 ^) is used to capture the impulse response ( For example, T3 in FIG. 37), 1024 / D sub-rate samples are used (for example, 1024/8 = 128 sub-rate samples in the current specific embodiment). That is, the current specific embodiment enables a convergence time of 300 ms to achieve 128 sub-rate sample values of the channel sub-rate pulse response (as seen in the Rin> eSin termination end of the echo canceller) and find its maximum value. Although described above, in different embodiments, different convergence times (ie, different size measurement periods N), different window sizes (ie, not limited to 1024 basic rate samples or 128 milliseconds), and different sub-rates (where D may be Any value, including 1) and a sampling rate other than 8 kHz. If the diamond 498 is being determined, the decision index η (which may be initialized to an initial value at the beginning of the call, such as 1 or 0) has not reached N, the flow proceeds to block 502, where η is incremented, and the flow continues to block 213 of FIG. However, if the diamond 498 is determined and η is equal to N, it means that 300 samples have been processed (corresponding to the duration of 300 milliseconds), the flow proceeds to block 503, where η is initialized to 1, and other measurement cycle variables are also initialized (such as PRinSR, PSinSR , Sr_near_end_detect_flag, etc.). The flow then proceeds to decision diamond 504, where it is determined whether the serial index is two. If so, the flow bypasses block 505 and proceeds to block 500. However, if the string 86017 -68- 200417167 column index is not 2, the flow proceeds to block 505, where the string index is incremented. As explained above with reference to FIG. 20, the serial index is used by the selective serial mode, wherein the serial signal is injected only at the beginning of a telephone call. Therefore, the tandem reference is used to indicate the beginning of a telephone call. The flow then proceeds from block 505 or decision diamond 04 to block 500, where the pure delay is estimated separately. It should be noted that separately estimating the pure delay corresponds to the pure delay estimated for each measurement period (that is, for every N sub-rate samples), which will be described in detail with reference to FIG. 22. After estimating the pure delay alone, the process proceeds to block 501, where several (2, 3 or more, depending on the specific implementation and depending on the call phase) effective separate pure delay estimates are used to determine the estimated delay of 13 °, which will be referenced Figure 23 illustrates this in detail. FIG. 22 illustrates a specific embodiment of the block 500 of FIG. 21 in which the delay is individually estimated. The flow begins at block 506 where the sub-rate echo return loss enhancement (SR-ERLE) is determined. The following formula can be used to determine sr_eRLE: Formula 24 ·· SR_ERLE (k) = 10 * l0gl0 (PsinSR⑻ / p⑽⑽⑻) Therefore SR_ERLE corresponds to the ratio between PsinSR and PerrorSR, which is used to verify pure delay measurement. SR-ERLE provides information about the convergence (good) of the sub-rate adaptive filter 122 (ie how many echoes have been eliminated). That is, a higher SR-ERLE (e.g., 5 dB or more) indicates that the adaptive filter 122 has sufficiently converged during the current measurement period. (It should be noted that the SR-ERLE can be determined by the ERLE estimator 116 operating at a given sub-rate, see Figure 7.) Therefore, after block 506, the process enters the decision diamond 508, where the SR-ERLE and the sub-rate ERLE limit In comparison, if it is not greater than this limit, the flow proceeds to block 514, which indicates that the current measurement period should not be used because of insufficient SR-ERLE. Therefore, the current 86017 -69- 200417167 measurement (for the current measurement cycle) is abandoned and the flow goes to block 501 in FIG. 21. However, if SR_ERLE exceeds the sub-rate ERLE limit, the flow proceeds to block 510, which performs another check on the convergence of the sub-rate adaptive filter 122. In block 510, the peak-to-average ratio (PAR) of the coefficients of the sub-rate adaptive filter 122 is determined. Referring to Figure 37, the peak corresponds to the maximum value (meaning that the peak value is the maximum distance in the positive or negative direction of the zero axis). The peaks in Figure 37 are indicated by the symbol _. The average value is calculated using the absolute value of the sub-rate adaptive filter coefficients. ′ If the PAR is not greater than PAR-Threshold, the flow proceeds from the decision diamond 512 to the square _ block 514, where the current measurement is abandoned because the current measurement cycle does not provide the appropriate convergence of the sub-rate adaptive filter 122. However, if the PAR is greater than PAR-Threshold, the flow proceeds to block 516, which indicates that to ensure that the sub-rate adaptive filters are sufficiently converged in the current measurement cycle, two conditions are satisfied. In block 516, the neutron rate adaptive filter 122 coefficient maximum value (corresponding peak value) and its corresponding time value (T peak value in FIG. 37) are located (which can be performed by the maximum value positioner 124 in FIG. 7). The flow then proceeds to block 501 of FIG. 21, which is described in detail in FIG. FIG. 23 is a specific embodiment of the block 501 of FIG. 21, which determines the pure delay _ estimate (corresponding to the delay decision 126 and the estimated delay 130 of FIG. 7). As mentioned above, pure delay is usually estimated at the beginning of a call and can change between calls if certain conditions are met. It is generally conservative to change the estimate of pure delay in the middle of a call because (a) statistics for telephone calls (PSTN calls and packet phone calls) indicate that pure delay does not change very frequently in the middle of a call, and (b) from the perspective of the phone user Changing the pure delay estimate too often (by being too close to tracking) can be destructive. Therefore, the process starts with the decision diamond 528, which determines whether this is 86017-70 · 200417167 / person, whether it is a child journey (that is, the beginning of a telephone call), or whether the previously estimated delay is equal to zero (which may correspond to the start of the call or (The middle of the grunt was previously estimated to be zero). If it is one of these two situations, the process enters a diamond shape, which determines whether two valid measurements are available. As explained above with reference to FIG. 22, each individual estimated delay is verified using both sr-erle & par, and only the individually estimated delay is verified as the corresponding delay measurement stored. Therefore, there is a possibility of obtaining another valid K for each measurement: cycle (every 300 milliseconds in the current specific embodiment). Assuming that there are at least two available (which are obtained using at least two measurement cycles), the flow proceeds to block 53o, where one of the estimated delays, the "fast track" calculation, starts at block 53o. In block 530, the 'first buffer is filled with two consecutive valid measurements. The flow proceeds to block 5 3 2 ′ where the two measurements and the dispersion between the averages of the two measurements are used. For example, dispersion can be the difference between two measurements. The process proceeds to a decision diamond 534, in which it is determined whether the dispersion is less than the dispersion limit and whether the average value is greater than the average value limit1. If not, the new estimated delay is not calculated and the flow proceeds to block 213 of FIG. However, if these conditions are met, the flow continues to block 542 'where a new estimated delay is calculated. Therefore, the dispersion limit value 1 and the average value limit value 1 ensure that the new estimated delay is calculated only when there is enough time between the two measurements. In other words, 'if the time difference between the subsequent impulse responses reaches its maximum value, the measurement delay is too large' until the subsequent measurements are more consistent (ie closer to each other). In block 542, the following formula can be used to calculate the new estimated delay: Formula 25: New estimated delay = average value · offset In the above formula, the average value corresponds to the average of the two measurements used in block 532. The offset corresponds to the actual response The amount of time -71- 86017 200417167 before the peak reached within the initial impulse response. That is, referring to FIG. 37, the peak corresponds to the time when the number of T4 is greater than T1 (pure delay). Therefore, T4 must be subtracted from the peak time (τ peak) value. The offset corresponds to this T4 value, and the offset can be determined and programmable into the echo canceller 20 using statistical information about the different but typical merge circuit impulse responses that occur in the area. The new estimated delay (corresponding to the estimated delay of 13) is then applied to block 544. For example, applying the estimated delay of 13 may correspond to the activation of the selective delay block 66 of Fig. 4, which is a specific embodiment of the adaptive filter unit µ. Therefore, by using pure delay estimation, the number of filter taps required by the adaptive filter unit 28 is reduced, because the coefficient used to respond to the pure delay portion can be regarded as zero. It should be noted that alternative embodiments may require more or less than two measurements in determining diamond 529 to continue the "fast track" calculation. In a specific embodiment, only one valid measurement is required. In this case, the dispersion and average values are not calculated (because only one measurement is used). Similarly, the actual value can then be checked for the average value limit i before deciding whether to proceed to block 542, while scatter comparison is not required. Instead of one embodiment, more than two valid measurements are needed, and the dispersion may correspond to the variance used in the valid measurements. Therefore, alternative embodiments may require any number of valid measurements. If the diamond 528 is being determined, this is not the first time the call has passed (usually means that pure delay estimation is performed in the middle of the call) and the previous delay estimation is non-zero, the process enters the decision diamond 535, which determines whether M valid measurements are available. For item U, the choice of M is 3, 4, or 5 (depending on the specific setting selected by the echo canceller installer). The value of M can be selected to require more or fewer measurements before it is possible to update (ie change) the current estimated delay value. The higher the number, the less 86017 -72- 200417167 flow enters block 536. Therefore, M can be selected to any value and is not limited to 3 to 5. If M valid measurements are not available, the flow proceeds to block 213 'of Figure 9 to bypass the possibility of changing the estimated delay value. However, if M valid measurements are available, the flow proceeds to block 536, where the second buffer is filled with μ consecutive valid measurements. In block 538, the dispersion, average, and difference between the average and the previous average are calculated. As mentioned above, dispersion can be calculated in various ways. For example, if M is only 2, the dispersion can simply be a difference. Alternatively, dispersion can be calculated as a change. The previous average corresponds to the average previously calculated when blocks 538 or 532 passed. After the calculation of block 538, the flow proceeds to a decision diamond 54, where various limits are used to determine whether it is worth changing the estimated delay. Therefore, determining the limit of diamond 540 can be used to establish more dialog criteria to change the estimated pure delay in the middle of a call. In determining diamond 540, the dispersion is compared with the dispersion limit 2, the average is compared with the average limit 2, and the difference between the average and the previous average is compared with the difference limit. If the dispersion is less than the dispersion limit 2, or the average is greater than the average limit 2 ', or if the difference is less than the difference limit, the flow proceeds to block 213 in FIG. 9 without calculating a new estimated delay (ie, maintaining the current estimated delay). However, if all of these conditions are satisfied (the dispersion is less than the dispersion limit 2, the average is greater than the average limit 2, and the difference is greater than the difference limit), the process proceeds to block 542 where a new estimated delay is calculated (as explained with reference to Equation 25) ) And apply it to block 544, as described above. Using "Fast Tracking", the dispersion limit 2 ensures that the M valid measurements will not deviate too much from each other, and the average value ensures that the M valid measurements are large enough to keep the need to change them (for example, if the average value is relatively small, no change Return to 86017 • 73- 200417167 The pure delay of the sound canceller. If the small pure delay is provided correctly, it can be adjusted by the adaptive filter 28). The comparison of the difference and the difference limit prevents the current estimated delay from changing if the difference is too small (ie, less than the difference limit) and therefore not worth changing. Figure 24 illustrates a specific embodiment of a selective monitoring mode that can be used to reduce MIPS (million instructions per second, a common standard used by digital signal processors) by the echo canceller 20. The flow of FIG. 24 is part of 211 of FIG. 9 and can be used to decide when to execute the flow of FIG. 21. The flow begins at block 518, where the film returns to the echo return loss enhancement (ERLE). ERLE corresponds to the “good” convergence of the adaptive filter 28 (ie, provides information on how many echoes have not actually been eliminated by the adaptive filter 28). The following formula can be used to calculate ERLE: Formula 26: ERLE (n) = 1〇 * i〇gl〇 (Psin⑻ / p⑽r⑻) ERLE therefore corresponds to the ratio between PSin and perr〇r, where the number of samples is read. (It should be noted that Psin and Perror can be calculated using the above formulas 1 and 2.) This ERLE value is therefore used in the monitoring mode used to enter the pure delay adjustment method of Figure 21. The process proceeds to decision diamond 520, where ERLE is compared to an ERLE limit. If the ERLE is greater than or equal to the ERLE limit, the convergence of the adaptive filter 28 is sufficient and no pure delay estimation needs to be performed, and the flow proceeds to block 213 in FIG. However, if the ERLE is less than the limit, the convergence of the adaptive filter 28 is insufficient and the process proceeds to block 521 where the ERLE counter is incremented. (It should be noted that the ERLE count is initialized at the beginning of the mother call.) The flow then proceeds to the decision diamond 523 ′ where the ERLE counter is compared with the ERLE counter limit. If the ERLE counter has not reached the ERLE counter limit, the flow bypasses block 522 (corresponding to the flow of FIG. 21) and proceeds to block 213 of FIG. However, if the ERLE counter has reached the limit of 86017 -74- 200417167 ERLE counter, the flow proceeds to block 522, where the entire flow of Figure 21 is performed (as described above). The flow then proceeds to block 524, where the ERLE counter is reset, and then the flow proceeds to block 213 of FIG. The ERLE counter and ERLE counter limits ensure that if the ERLE frame calculated in block 518 (changes from the above ERLE limit to below the ERLE limit occurs too frequently), the pure delay will not be recalculated and updated. That is, the ERLE must fall below the ERLE limit for a period of time (controlled by the ERLE counter and the ERLE counter limit) before entering the process in Figure 21. This helps prevent rapid and undesired changes in pure delay estimates. 8 and 25 to 27 are specific embodiments of audio detection, which can be used in the echo canceller 20, wherein FIG. 8 illustrates a part of the monitoring and control unit 30 in the form of a block diagram, and FIGS. 25 to 27 are flowcharts. Formally illustrates a portion of block 209 of FIG. When at least one of the echo canceler 20 inputs (such as Rin 44 or sin 38) is audio, the stability of the adaptive filter 62 may be affected, leading to undesired distortion and degradation of service quality in the telecommunications network. Audio is a signal consisting of sinusoidal components that have a constant value, frequency, and phase for a specific period of time. Any adaptive algorithm that attempts to minimize the average power of the residual echo (eg, used by the adaptive filter 62) will have a dynamic behavior that depends on the autocorrelation matrix of Rin 44. Receiving path signals of a particular class can make this matrix singular 'which can temporarily degrade the adaptive method and deviate the filter coefficients of the adaptive filter 62 from required values. For example, a sinusoidal signal (single frequency audio) can cause this situation. In this case, the autocorrelation r (k) of the sinusoidal signal Rin (n) = Acos (i2n + 沴) is provided by r (k) = A2cos (Qk) / 2, most of which are practical in the case of 86017 -75- 200417167 Results in a singular autocorrelation matrix (ie when its size is large). At this time, the possible result of the adaptive algorithm is a set of sine form filter coefficients (for the adaptive filter 62), which is an error estimation of the impulse response of the actual combined circuit. Figure 37 provides an example. Similarly, multi-frequency audio can also cause similar problems, because when the number of components is not large enough or the matrix has a large size, their autocorrelation can still generate a singular autocorrelation matrix. It should be noted that the size of the matrix depends on the number of filter coefficients used to estimate the impulse response of the combining circuit. Therefore, it is necessary to detect the presence of any signalling and control audio, and then terminate the adaptation method of the adaptive filter 62 to prevent divergence from a good set of filter coefficients. A specific embodiment described with reference to FIGS. 25 to 27 uses a polynomial filter, such as a modified version of the Teager_Kaisef filter for indicating the presence of a sinusoidal signal of any frequency, a method for identifying a smooth correlation of a predetermined single frequency audio, and Decision logic for reliable audio detection. It should be noted that any suitable polynomial filter can be used. The polynomial filter in Figure 8 is an example. Although the specific embodiments described herein are generally referenced to the echo canceller 20, they can be used with any device or telecommunications device that requires audio indication and detection, and are not limited to echo cancellers. Figure 8 includes a specific embodiment of the power estimator 134, which maps any single frequency audio to a constant by modifying the energy operator. That is, a single frequency audio can be expressed as follows. Equation 27: x (n) = Acos (Qn + more) Modifying the energy manipulator Tk can be expressed as follows. Formula 28: ⑻) = X2 (n one moxibustion) one one two moxibustion) sin2 (ΑΩ) 86017 -76- 200417167 In the above formula, it should be noted that x2 (n-k)-x (n) x (n -2k) corresponds to The output of the adder 144 in FIG. 8 (that is, the output of the delay 136 is x (n-k), the output of the delay 138 is x (n-2k), and the output of the multiplier 140 is x (n) x (n-2k ), The output of the multiplier 142 is x2 (n-k), and the output of the adder 144 is the sum of the output of the multiplier i42 and the output of the multiplier 140). It should be noted that the input signal χ (η) can correspond to Rin 44 or Sin 38. In addition, substituting χ (η) of Formula 27 into x2 (n-k) -x (n) x (n-2k), the result A2sin2 (kD) is obtained. It should be noted that Ψ depends on the value A and the audio normalization frequency ω (Ω = 2πί / [, where f is the audio frequency and fs is the sampling frequency, which is 8 kHz in a specific embodiment). The parameter k in these formulas defines the basic sub-rate processing, where k can be any integer value, including 1. Therefore, applying Vk at the sampling rate fs is equal to applying Ψ1 at the sampling rate fs / k. As mentioned above, sub-rate processing can be used to reduce computational requirements, where only every k-th sample is processed. (Note that ψ) < :( χ (η)) does not depend on the initial phase 0, but generates a short-term transient based on a steep phase change, which can be used to detect a phase change in the communication signal χ (η). The x (n) power (Equation 27) can be expressed using the following formula. EQUATION 29: Powerx⑷ = A2 / 2 Therefore, pay attention to Ψ1 <: (χ (η)) provides the power of χ (η) scaled by 2sin2 (kD) such that: Formula 30: Ψ1 < :( χ (η)) = Powerx (n) * 2sin2 (kQ) So solving Powerx according to Vk (x (n)) provides the following formula: Formula 31: P〇werx⑷ = Ψ1 < :( χ (η)) 〇8 (: 2 (1 <: Ω) / 2 However, in practice, the signal x (n) (which may correspond to Rin 44 or Sin 3 8 in the specific embodiment illustrated in FIG. 8 as described above) may be damaged by noise, generating a noise estimate ψ 86017 -77- 200417167 Noise k (x (n)). Any low-pass filter can then be used to smooth the result, such as a single-pole low-pass filter. Therefore, as seen in FIG. 8, the power estimator 34 includes an output that receives the value 146 (corresponding to the absolute value of the output of the adder 144) and a low-pass oscillator from a of the storage 150 and provides a smooth estimation of ψ noise P (n). P (n) can be expressed as the following formula: · Formula 32 ··-1) + (1-ψ2 («One, in the above formula, a is a smoothing parameter that controls the bandwidth of the smoothing low-pass filter · (〇 < a < l). It should be noted that a fixed or variable smoothing parameter may be used and then ρ (η) φ is provided to the audio instruction determination unit 166 of FIG. 8, which indicates whether audio exists based on the variance of the estimated p (n), which will be described in detail below with reference to FIG. 26. Although Figure 26 refers to power, other functions of the communication signal can be used, such as correlation (see Figure 27), or even the communication signal itself. Once an audio is present, a specific embodiment detects any predetermined single frequency audio with or without phase reversal, such as 2100 Hz signaling audio. A specific embodiment for detecting predetermined audio will be described in detail below with reference to FIG. Therefore, a specific embodiment may only include the audio detection of FIG. 26, instead of the specific embodiment, as shown in FIG. Include cross-references between the algorithms of Figures 26 and 27. In addition, the specific embodiment of FIG. 26 can also be used in the monitoring mode to restart the adaptive method of the adaptive filter 62 after receiving audio. That is, the conversion between the transmitted audio signal and the voice signal can also use P (n) variance detection (that is, the conversion is detected when the variance is greater than a predetermined limit). Given the estimate p (n) according to the flow of FIG. 26, the audio instruction determination unit 166 may be used to detect audio. The time interval of the mud path detection p (n) variance in Fig. 26 is small. Whenever a single frequency audio appears on x (n), it is expected to correspond to a small square of p (n) 86017 • 78- 200417167 The difference of P (n) (constant foot level. If the audio is composed of multiple frequencies, p (n ) The variance will increase 'but the average level will remain constant. Therefore, single or multiple frequency audio can be indicated according to the variance level. The flow in Figure 26 therefore begins at block 588, where k, a, m, r, Plow, and Mmin are set to Required values. Depending on the desired audio frequency range and the level of noise in the system, these values can be, for example, k = 2, a = 0.9, m = 1, r = 0.95, PlQw = 2-8. Nmin depends on The sampling rate and the minimum required duration for detecting audio. The process enters a decision diamond 59, which determines whether P (n) is greater than PlQW, where Piqw corresponds to the limit indicating the lowest signal level (to be considered). If not, the process Go to block 598, where the detection counter is reset (zero) ', then go to block 604, indicating that the audio is not detected, and then go to block 554 in Figure 25. However, if P (n) is at least greater than Pl () w, The process proceeds from decision diamond 590 to block 592, where Pmin and Pmax are calculated. Pmin corresponds to m samples The minimum of the two estimates of separated P (n), and Pmax corresponds to the maximum of the two estimates of P (n) separated by m samples. Formula 33: Pmin = MIN (P (n), P (n -m)) Formula 34: Pmax = MAX (P (n), P (n-m)) The variance level is estimated by comparing Pmin and Pmax. Therefore, the process enters to determine diamond 594, where the ratio of Pmil ^ Pmax (ie Pmin / Pmax) is compared with the audio indication limit. If it is not greater than the audio indication limit r, the flow proceeds to block 598, where the detection counter is reset, and then the indication audio is not > Block 554 of 25. However, if Pmin / Pmax is greater than the audio indication limit, then P (n) is considered sufficiently constant (ie, Pmin and Pmax are sufficiently close) to indicate the possibility of audio. In this case, the flow proceeds to block 596. Where the chastity counter is incremented (it should be noted that the detection counter can be initialized or reset at any other appropriate time before the call begins or before entering the flow of 86017-79-200417167 Figure 26). The flow enters decision diamond 600, where the detection is determined Whether the counter is greater than Nmin. If the detection counter has not yet reached, then The flow enters block 604 indicating that the audio is not detected, and then proceeds to block 554 in FIG. 25. However, if the detection counter is greater than Nmin, the flow proceeds to block 602, where audio is detected (which is the same as the audio indicator signal in FIG. 8). The judgment of 168 is consistent). The flow then proceeds to _ block 554 in FIG. 25. Therefore, audio is detected when p (n) is greater than the minimum level (Piqw), the variance of p (n) is less than the minimum value (about the audio indication limit), and when the detection counter is greater than the minimum value Nmin. The detection counter ensures that the audio is detected and it is determined that the audio has been present for at least a specific amount of time (corresponding to ^ _) before the audio indicator signal 168. This helps prevent rapid exchanges between detected and undetected audio, which can cause the adaptive method of adaptive filter 62 to be turned on or off too frequently. FIG. 8 includes a specific embodiment of the smoothing correlator 152. This correlator can be used in a variety of ways including the detection of any predetermined single frequency audio, the detection of the amplitude modulator signal carrier, and the detection of multi-component audio with frequencies close to the nominal frequency. The smoothing correlator 152 receives samples of the input signal χ (η) (as described above * which may be Rin 44 or Sin 38) and three control parameters b, b, and e from the storage 150 and generates two correlation estimates. These correlations are used to indicate the presence of predetermined audio, as will be explained below. The control parameter c defines one of the coefficients of a second sequential digital booster w (n), which generates a predetermined single frequency audio with a normalized frequency = 2wfd / fs, where fs is the input sampling frequency as above. The state of the vibrator is initialized to w (-l) = 1 and w (-2) = c = cos (Qd), and is used by w (n) = 2 * c * w (n-1)-w (n _ 2) Provided standard 86017 -80- 200417167 quasi-second sequential digital oscillator. (It should be noted that the oscillator may correspond to the oscillator 164 ′ of FIG. 8 and receive c and provide w (n) as an output to the multipliers 156 and 158.) Input the x (n) sign and the delayed version x (n_e) ( That is, the output of the delay I ”of FIG. $ Is related to w (n) (by multipliers 156 and 158) and then passed through a low-pass filter (ie, low-pass filter 160 of FIG. 8 to receive the output of multiplier 58), which is available x (n) w (n) represents, and the low-pass filter ι62 of FIG. 8 receives the output of the multiplier 156, which can be represented by x (ne) w (n). The parameter b is provided as an input to the low-pass filter 160. And 162, of which < b < l defines the bandwidth of the low-pass filter. Similarly, a specific embodiment of the smoothing correlator 152 may use smoothing single-pole low-pass filters as the low-pass filters 160 and 162. Similarly, in an alternative embodiment, the oscillator signal w (n) and the delayed version χ (η _ e) may be related to χ (η) instead of w (n) and χ (η) and χ (η- e) relevant. Also, in a specific embodiment, the delay factor is expressed as follows. Equation 35: e = 2Ω ^ The above equation corresponds to approximately 90. The phase difference, where | V | represents the smallest integer greater than or equal to its argument X. Referring again to FIG. 8, the output of the low-pass filter 160 is a correlation estimation rule ^ ... and the output of the low-pass filter 162 is a correlation estimation Rjn), both of which are provided to the audio instruction determination unit 166.仏 ⑻ and heart ⑻ can be expressed as ⑻ = b · R〇 (n -1) + (1- b) · w (n) · x (n), and RKn) = b · RKn -i) + b) · W (n) · x (n-e). Therefore, if the unknown audio has been instructed by the audio instruction determining unit (using the above-mentioned flow of FIG. 26), (η) uses the flow analysis of FIG. 27 to identify the existence of a predetermined single-frequency audio (corresponding to the oscillator 164). 86017 -81- 200417167 In order to detect specific audio, the process in Figure 27 correlates the detected audio to a predetermined single frequency audio. In Fig. 27, the flow starts at block 606, where c, e, b, u, q, and Mmin are set to the required values. The parameter is directly related to the frequency of the detected target audio ' It also defines the delay value e. According to the noise level in the system, the remaining value can be, for example, b = (K9, u = 1, q = 0.95, P1 () w = 2 · 8. Mmin depends on the sampling rate and the minimum need to detect the target audio Duration. The flow proceeds to block 608 'where R (n) and Rmh ^ Rmax are evaluated, as shown in the following formula. Formula 36: Outward 々 (4W4) Formula 37 ·· Rmin = MIN (R (n), R (n _ u)) Formula 38: Rmax = MAX (R (n), R (n _ u)) R (n) refers to the correlation between the peak values. Corresponding to two R (n) separated by u samples The minimum value of the estimate, and the maximum value of the two estimates of R (n) separated by ^ samples. The process then proceeds to determine the diamond 610, where the ratio Rmi3 + Rmax (Rmin / Rmax) and the correlation limit "its In a specific embodiment, the comparison is set to 0.95). If the ratio is not greater than the correlation limit, the flow proceeds to block 610, where the correlation counter is reset (zero), and then proceeds to block 618, which indicates that the predetermined frequency is not detected (Dr = 〇), and then proceed to block 560 in Figure 25. However, if the ratio is greater than the limit, the flow proceeds to block 612 where the correlation counter is incremented. (Note that correlation The counter can be initialized or reset at the beginning of each call.) The process enters the decision diamond 614, where the correlation counter checks Mmin. If the correlation counter is not greater than Mmin, the process proceeds to block 618, which means that the predefined frequency is not detected (Dr = 〇 ). However, if determining diamond 614, determine whether the correlation counter is greater than Mmin, the process enters decision diamond 620, where it is determined whether r 决定 is equal to R〇⑻ 86017 -82-200417167 absolute value. If so, the process proceeds to block 622, where a ruler is used. Slave ... symbol detection predefined frequency, ie Dr = sigr ^ R〆 !!)). If not, the flow proceeds to block 624 'where the R! (N) symbol is used to detect the predefined frequency, ie Dr = sigj ^ Rjn) ). The flow proceeds from block 622 or 624 to block 560 in FIG. 25. Therefore, as with the flow for detecting any audio illustrated in FIG. 26, when used for a predetermined amount of time R () defined by the correlation counter and Mmin n) Detection of predefined audio with very small variances. This helps prevent rapid exchange of detection counters and Nmin as described above. The method of Figure 27 is equivalent to using the validity given by the following formula Sliding correlation: Formula 39: You # («) = 1/2 ^ (+ Formula (Ca⑷yi. (+ Ruler) The above formula is generated based on the largest numerical component. Figure 25 illustrates the overall method flow including the flow of Figures 26 and 27. A specific embodiment 'wherein FIG. 25 illustrates a portion of block 209 of FIG. 9 according to a specific embodiment. In Figure 25, the flow starts at block 550, where the minimum counter values (Lmin-p and Lmin_n) are selected for Dpositive (Dp) and Dnegative (Dn), respectively. These values are selected to meet the required minimum and positive phase durations. Corresponds to a positive phase counter and Dn corresponds to a negative phase counter. The flow then proceeds to block 522, where a search is detected for any single frequency audio. The flow of Figure 26 can be used to determine the existence of any single frequency audio. The flow enters the decision diamond 554. If no audio is detected, the flow proceeds to block 558, where the Dp and Dn counters are reset (zero) and the flow enters block 582, which indicates that the audio is not detected, and then proceeds to block 211 in FIG. However, if an audio is detected, the flow proceeds to block 5 5 6 where the detected audio is related to a predetermined single-frequency audio. Therefore, the flow of FIG. 27 can be used to execute blocks 5 5 6. The flow 86017 -83- 200417167 enters the decision diamond 560, which determines whether Dr is zero. If so, the predetermined frequency is not determined at block 556 (e.g., block 618 of Fig. 27), and the flow proceeds to block 558 where the counters Dp and Dn are reset. If not, however, the process proceeds to a decision diamond 562, which determines whether Dr is greater than zero. If so, the flow advances to block 4 where the positive phase counter is incremented; otherwise, the flow advances to block 566 where the negative phase counter is incremented. After block 564 or 566, the flow passes point g to block 568, where Flagpositive (Fp) and Flagnegative (Fn) are reset (zero). The process proceeds to decision diamond 570, where if Dp is greater than Lmin.p, Fp is set to one at block 572, otherwise the process bypasses block 572 and enters decision diamond 574. The decision diamond 574 determines whether Dn * is greater than Lmin-n 'if yes' Fn is set to one at block 576. If not, the flow bypasses block 576 and proceeds to decision diamond 578. In determining diamond 578, if Fp and Fn * are zero (that is, if FP + Fn is zero), the flow proceeds to block 582, indicating that the audio is not detected. That is, if no counter (Dn or Dp) is greater than a certain minimum value (for example, Lmin-p or Lmin-n, respectively), the Ui does not detect that audio is needed. However, if Fp + Fn is not zero, the process proceeds to a diamond 580 representing a decision to detect audio. If there is only one counter greater than Lmin (Dp or Dn), Fp + Fnf will be greater than one, and the flow proceeds to block 584, which indicates that the audio required for detection is inverted without correlation sign. If both Dp and Dn are greater than their respective Lmins, the flow proceeds to block 586, which indicates that the audio required for detection is inverted using the correlation symbol. If the R (n) average level is the same during the correlation sign inversion, it indicates that the phase is inverted. Therefore, the flow of FIG. 25 combines the flows of FIGS. 26 and 27 and detects a phase inversion. An alternative embodiment recognizes a phase change (not necessarily 180) within a given single frequency audio by detecting a sharp change in P (n). -84-86017 200417167 It should be noted that the description up to this point has assumed that there is no non-adaptive filter 64 in the adaptive filter unit 28 (see FIG. 4), so the reference or coefficient of any adaptive filter 28 The adaptive filter 62 in the reference adaptive filter unit 28 is similar. Therefore, in the previous description, it is not necessary to refer to the adaptive filter 62 separately from the adaptive filter unit 28. However, in the following description of FIGS. 28 to 36, the non-adaptive filter 64 may exist and be considered as part of the adaptive filter unit 28. Therefore, the coefficients of the adaptive filter 62, as used throughout the above description, since the adaptive filter unit 28 may include a combination of various filters (such as the adaptive filter 62 and the non-adaptive filter 64), The coefficients or taps of the adaptive filter 62 will now be more specifically referred to. As mentioned above, the adaptive filter 62 tracks the echo introduced by the merging 16, and thus usually requires a large number of taps. For example, in order to follow the entire pulse response of FIG. 37, the adaptive filter 62 (assuming a sampling rate of 8 kHz) of the adaptive filter unit 28 requires 256 taps, which spans 32 milliseconds to cover the entire pulse response. As the number of taps of the adaptive filter 62 increases, the computational complexity increases and the convergence speed generally decreases. The method described above with reference to Figures 20 to 24 provides a purely delayed debt test so that the adaptive filter 62 uses a sparse window covering the impulse response after pure delay. The method described below with reference to Figs. 28 to 36 is about a mechanism for shortening the range of the echo path. That is, in addition to detecting fine-tuning pure delay, it also detects and shrinks dispersion time to shorten the echo path range of the impulse response. As will be explained, a specific embodiment is to add a fixed or adaptive chirper for the purpose of the number f to reduce the residual echo to a minimum. As illustrated in Figure 37, the impulse response is really zero and poles. Zero prevents the corresponding frequency response, while poles enhance the corresponding frequency response. Therefore, by adding a filter-85- 86017 200417167 filter or a filter to compensate for the poles, the impulse response can be compressed and a smaller number of taps are required. For example, assuming that the IIR filter has a transfer function H (z) represented by the ratio b (z) / a (z), the filters A, (z) can be designed to compensate for the poles of H (z) such that H (z) * A '(z > B (z). A specific embodiment illustrated in FIG. 2 uses selective non-adaptive filters 3 1 and 35, so that the output of non-selective filter 3 (that is, sin 39) is equal to and The echo of the non-adaptive filter 3 1 convolution. However, the existence of the non-adaptive filter 3 1 immediately following the notch wave> Jie Bo Yi 4 5 introduces distortion into the Sin 37 that needs to be compensated. Therefore 'non-adaptive The filter 35 may introduce a reception error signal 46 and generate a filtering error signal 47. It is assumed that the non-adaptive filter 31 is a FIR filter having a transfer function A '(z), and the non-adaptive filter 35 is a transfer function 1 / A, ⑺ inverts the IIR filter. However, A, ⑻ needs to be limited because the zero of A, (z) of the FIR filter 31 becomes the pole of 1 / A '(z) of the inverting IIR filter 35. These The limitation of zero of A '(z) will be further explained below, which prevents the poles of the non-adaptive IIR filter 35 from amplifying the error signal 47. Alternative embodiments may use non-suitable Different configurations of adaptive filters. For example, 'non-adaptive filter 35 is not placed at the output of the adder 34, but is placed before the adder 34 and is at the outputs of the non-adaptive filter 31 and the adaptive filter 62 (which can be Produces the same network effect). In this specific embodiment, the non-adaptive filter 3 1 immediately next to the non-adaptive filter 35 can effectively eliminate the echo, so that no filter is needed between Sin 38 and Sin 39 (that is, Sin 39 And Sin 38). The adaptive filter 28 can then be designed to include a selective non-adaptive filter 64 (similar to the non-adaptive filter 35). Therefore, only one additional filter is required in a specific embodiment. However If the IIR filter is used for the non-adaptive filter 64, the stability limitation is still required. That is, all the roots of the number 86017-86-200417167 defined by the coefficients of the filter 64 should be less than one (that is, within the unit circle), below It will be explained in detail. It should be noted that for the transfer function H (z) = W, the root of W corresponds to zero of H (z) and for H (z) = 1 / W, the root of W corresponds to H (z). Pole. Selective filters 31, 35, and 64 do not have the same coefficients as the main The adaptive filter 62 is periodically non-adaptive when it is adaptive. Usually they can be regarded as adaptive filters whose rate is driven by events. '· Fig. 28 is a block 213 of Fig. 9 according to a specific embodiment of the present invention. Part of the process. The process begins with a decision diamond 626, which determines whether to start adaptive filter repair shortening estimation. If not, the process bypasses the process of Figure 28 and continues to block 212 of Figure 9. However, if started, the process Enter decision diamond 628. The adaptive filter shortening estimation can be started in a variety of different ways. For example, it can start automatically, such as in response to a system reset. Alternatively, it can be activated at any time with different delays in the delay unit 66 (if present) of Figure 4 or whenever a new merger is detected. The coefficients selected for non-adaptive filter 64 or non-adaptive filter 詻 31 and 35 depend on the specific union 16, because each different union can have a different impulse response, which has a different pure delay or a different dispersion time. In a specific embodiment, at the beginning of a call or based on changes that affect mergers (for example, based on call transfer or call transfer), the pure delay estimation described above with reference to FIGS. delay. To obtain the filter coefficients for the non-adaptive filter 64 or the non-adaptive filters 31 and 35, the method described with reference to FIG. 28 is used to determine both pure delay and dispersion. The pure delay calculation in Figure 28 is usually more accurate, however, it usually takes a long time to decide. Therefore, in addition to reducing the effective number of coefficients required by the adaptive filter 62, the method of Fig. 28 can "fine-tune" the pure delay estimates provided by Figs. 20-24. The 86017-87-200417167 method of Figure 28 determines that any delay element 66 needs to be added to compensate for the additional pure delay of the additional filter (64 or filters 31 and 35). That is, as will be explained below, increasing the filter for shortening the dispersion time also attempts to slightly increase the number of pure delays, so the delay of the 'delay unit 66 (originally determined by the method of Figs. 20 to 24) can be updated. If the monitoring mode of FIG. 24 is used, each time the ERLE decreases below the ERLE limit ', a new pure delay for the delay unit 66 is determined. In addition, the adaptive filter shortening estimation option of Fig. 28 can be activated in response to the ERLE falling below the ERLE limit (that is, in response to the new pure delay determined for the delay unit 66 by the flow of Fig. 21). In an alternative embodiment, the processes of FIGS. 20 to 24 may shorten the estimation option without an adaptive filter; or, similarly, the adaptive filter shortening estimation option may be used in an echo canceller without the pure delay of FIGS. 20 to 24. Estimation method. Alternatively, in the echo canceller having the method of Figs. 20 to 24, the adaptive filter shortening estimation option may be activated independently of the method of Figs. 20 to 24. Similarly 'if option is not enabled' (or if option is still used to determine new coefficients for adaptive filter 62 and coefficients for additional non-adaptive filter 64 or additional non-adaptive filters 31 and 35), additional filters or filters The filters can simply be bypassed (or they can pass the signal without filtering). If the option is activated in decision diamond 626, the process enters decision diamond 628, which determines whether ERLE 疋 is good enough. (ERLE can be calculated as the above formula 26, where ERLE corresponds to the ratio between PSin and perror, and where pw and dagger can be calculated using the above formulas 1 and 2.) To determine whether erle is good enough, it can be compared with a limit Compare. For example, the limit can be set to a value greater than 20 dB, or it can be set in the range of 30 to 40 dB. Generally, the higher the ERLE, the better the signal 86017 -88- 200417167 (because the lower the error perrcr). ERLE is high enough when no Sgen (proximity talker signal) is present, because otherwise ERLE decreases. Alternatively, it may determine whether a near-end talker signal exists from the above-mentioned near-end signal detector 26 before continuing to determine whether ERLE is present and comparing a limit value. If there is a near-end talker signal, or if ERLE is not good enough, the process bypasses the rest of FIG. 28 and continues to block 212 in FIG. However, if the ERLEs are good enough (above the limit), the flow proceeds to block 630. That is, the adaptive filter shortening estimation option should be performed when a good signal is present and an erroneous signal 46 is obtained from the good echo estimate 48. It should be noted that in alternative embodiments, many signals in the system may be used to determine whether a good signal for the execution option is present. In block 630, a pure delay and dispersion based on the current coefficients of the adaptive filter 62 is determined. (It should be noted that block 630 will be explained in detail with reference to FIG. 29.) After block 63, the flow proceeds to block 632, where the coefficients of the additional non-adaptive filter 64 or filters 31 and 45 are determined according to the pure delay and dispersion determined in FIG. 63. w and new coefficients corresponding to the new shortened version of the adaptive filter 62. (It should be noted that block 632 will be described in detail with reference to FIG. 30). The flow then proceeds to decision diamond 634, which determines whether the new configuration is good enough. That is, different criteria can be used to determine whether the new configuration is satisfactory. For example, in a specific embodiment, if the number of new configuration coefficients after reduction is still greater than the number required to reduce the coefficients, the method of block 632 may be repeated to obtain a further reduction in the coefficients. Alternatively, it may be decided that the diamond 634 does not exist so as to perform only one-item iteration, and the result of the block 632 is deemed sufficient. That is, the decision made in block 632 is used. If the new configuration of the diamond 634 is determined to be good enough, the flow continues to block _ 'where the adaptive filters 62 are regrouped and cheap. 86017 -89- 200417167 The new coefficients of the adaptive wave filter 62 are loaded into the adaptive filter 62 and the adaptive filter 62 is used to combine the non-adaptive filter 64 or the combined non-adaptive filter 31 and 35. (It should be noted that block 636 will be described in detail with reference to FIG. 31). Note also that the delay value in the 'delay unit 66 in block 636 can be updated by adding any delay required (adding a non-adaptive filter to the existing delay value in delay 66). Flow then proceeds to block 638, where the filter shortening estimation option is turned off. That is, the flow of FIG. 28 is usually not performed on a per sample basis. It is performed only when needed ', such as in the case described above with reference to an example of an example of adaptive filter shortening estimation options. However, in an alternative embodiment, the flow of FIG. 28 may be performed on a per-sample basis. It should be noted that in the specific embodiment illustrated in Fig. 28, blocks 630 to 638 are executed continuously after ERLE is determined to be sufficiently good. However, in alternative embodiments, blocks 630 to 638 (or a subset of blocks 630 to 638) may be started as separate threads that execute in parallel with other tasks of the echo canceller 20. After completing the flowchart, the method of FIG. 28 may warn the echo canceller 20 that the results are ready to update the adaptive filter 62. Alternatively, it may warn the echo canceller 20 that the entire filter shortening has been completed. For example, a signal or An interrupt can be provided to the echo canceller 20 to indicate completion of blocks 630 to 638 (or a subset of blocks 630 to 638). Figure 29 illustrates a portion of block 630 of Figure 28. That is, the flow of Figure 29 is purely delay Here comes a specific embodiment of the dispersion of the current coefficients of the adaptive filter 62. The flow starts at block 640, where the value of the coefficient of the adaptive waver 62 is moved to the ring buffer. That is, the adaptive filter 62 filter is adopted A snapshot of the coefficients is stored in a circular buffer of size ^, which has positions 0 to N-1. The current coefficient η of the adaptive filter 62 is available η = [h〇, ..., 86017 • 90- 200417167 hN -i] & shows' where h❹, ···, hN i corresponds to the coefficient and n corresponds to the adaptive filter 62 coefficient or the number of taps. Therefore, the value corresponding to w is stored in the position "n MOD N" in the ring buffer. Where n corresponds to the number of samples and r | x | "indicates" x value "(and corresponds to a positive value). The expression "n MOD N" corresponds to the η modulus, which refers to the remainder of the operation η / N. For example, if ν is 256 and n is 270, n MOD Ν refers to 14 ′ where | center. | From "position 27o" (which is outside the ring buffer of size n) to position 14 of the ring buffer. Therefore, if the value of η is greater than n, the value of N can continue to be subtracted from η until η falls within the range of 0 to ν-1. Similarly, if the value of η is less than 0 ', the value of ν can be continuously added to η until η reaches a range of 0 to N-1. The process then proceeds to block 642, where for each coefficient, h, energy E (η) (where η = 0 to N-1} is taken as the sum of the numerical values in the sliding window of the size LW. In a specific embodiment, LW is the length of the target window size, that is, the number of taps or coefficients after reducing the effective number of coefficients of the adaptive filter 62. For example, in a specific embodiment, LW can correspond to a sliding window with a size of 10 samples, where Each tap system needs a filter length. Therefore, if] ^ is 256 (representing the 256 coefficient h of the adaptive filter 62), then determine the 256 value of E (n), where each 256 value of E (n) It is the sum of 10 values (corresponding to 10 samples of LW). E (n) can therefore be expressed as the following formula 40. Formula 40: five W = where 11 = 〇, 1, ..., N-1 / * 〇 In the above formula (and other formulas described in this article), it should be noted that the symbol [χ] N corresponds to X MOD Ν. Then the flow proceeds from block 642 to block 644, where the delay D is set to the position of the energy peak negative LW. That is to determine e (sliding window) Internal energy of LW) After the value of N of 86017 -91- 200417167, the point where the largest e appears negative LW (at the time of sample ) Corresponds to the pure delay of the impulse response. Therefore, D can be expressed as shown in Equation 41. Equation 41: D = arg (max E (n))-LW where n = 〇, 1, ..., n-l In the above formula, The maximum E (n) refers to the maximum value of E among the N values of E, and arg (maxE (n)) refers to the parameter or point where the maximum E appears, where "parameter" corresponds to the sample time. Therefore, D corresponds to a pure delay. The process continues Go to block 646, where the dispersion time corresponds to the number of samples between d and the next position (where E is less than the predetermined limit). That is, E (n) (corresponding to the impulse response value) is in the range η == 0, 1, ... · The general trend of N-1 can be described as generally increasing toward the maximum peak and then decreasing back. Therefore, after reaching the maximum value, Ε (η) decreases, which corresponds to points outside the predetermined limit range The end of the dispersion time, where the dispersion time is thus the number of samples between D and the point (sample time) after which E (η) reaches its predetermined limit. The predetermined value can be set to any value indicating the end of the dispersion time. For example, In a specific embodiment, it can be set to 192 samples (that is, 24 milliseconds at an 8 KHz sampling rate) Fig. 30 is a specific embodiment of the block 632-part of Fig. 28, in which (1) the non-adaptive filter coefficient W (for the non-adaptive filter 64 or the non-adaptive filters 31 and 35) and (2) A shortened version of the adaptive filter 62. The process starts at block 648, where a new filter coefficient w is determined (for the non-adaptive filter 64 or the non-adaptive filters 3 1 and 35) (note block 648 (Details will be described with reference to FIGS. 32 to 36). The process then proceeds to block 650, where the coefficient w is the current adaptive filter 62 coefficient convolution (that is, using the snapshot taken in block 640 of Figure 29) to determine the shortened filter coefficient B, where B includes the addition of a non-adaptive filter 64 or New coefficients for adaptive filter 62 after 31 and 35. The flow proceeds to 86017 -92- 200417167 block 652, where the new pure delay D and the dispersion time of the filter coefficient B are shortened. Therefore, the process of FIG. 29 and equations 40 and 41 can be used to complete block 652. The flow then proceeds to block 654, where from B (that is, a portion of b with a predefined length), the new adaptive filter coefficients for the adaptive filter 62 are determined and the maximum number of adaptive filter coefficients is selected (i.e., samples in the B selection section) Quantity). Fig. 31 illustrates a specific embodiment of block 636-Shaofen of Fig. 28, in which the adaptive filter 62 is reconfigured. The flow begins at block 656, where the shortening factor previously determined at block 654 of Figure 30 replaces the current adaptive wavelet coefficient Η. The flow proceeds to block 658, where the new coefficient w (determined in block 648 of Fig. 30) is loaded with a non-adaptive filter (filter 64 or filters 31 and 35). Therefore, by loading W into a non-adaptive filter or filter, they can be activated so that the adaptive filter 62 has a reduced filter length. Flow then proceeds to block 660, where the delay unit 66 within the adaptive filter 28 is updated with the new delay. For example, a specific embodiment may simply update the delay unit 66 with the new decision pure delay d. Alternative embodiments may determine the delay stored in the current delay unit% and update the existing value as needed. Alternatively, if the new delay has not changed much from the existing delay in the delay unit 66, the delay unit% may not be updated at all. Similarly, in block 660, once the adaptive filter & loads the new coefficients in block 656, it must be configured to adapt the number of new coefficients. Fig. 32 is a specific embodiment of block 648 of Fig. 30, in which a new filter coefficient W (corresponding to filter 64 or filters 31 and 35) is determined. AZ diamond 662 'which determines whether any pre-calculated filter coefficient% is: For example, there may be a library that has been pre-calculated, which includes various corresponding combinations of different combinations and -93- 86017 200417167 w, so the new w can be simply selected from this library, and the process can be borrowed By convolving the adaptive filter coefficients from all the existing Ws in the library and taking the one that provides the best performance, enter box 65 of FIG. 30. Alternatively, a single pre-calculation w can be used that best represents different conditions. Therefore, w can be calculated off-line in a variety of different ways, one of which will be described with reference to Fig. 36, so that pre-calculation W can be used. If the filter coefficient W is not pre-calculated, the flow proceeds to block 664 'where w can be found using any method that determines the new filter coefficient. Various specific embodiments for determining W will be described in detail with reference to Figs. 33 to 35. Flow then proceeds to block 666, where the root of W is determined. For example, w can be expressed as W = [W0, W1, ..., WM "], and its towel w., W1, ..., ~ corresponds to the filter coefficients and M corresponds to the number of filter coefficients, so that w (z) can be expressed Equation 42 is shown below: Equation 42: W (z) = (wozM-k WlzM-2 +… + Wm 2Z + Wm i) z1.m To determine the root of W, set W (z) to 0 and solve z, where z has an answer of Mβ1. Therefore, the root ruler of the hip (2) can be expressed as ruler = [1 * 0, 1 ^. ", ~ _1], where the root includes a complex number and its conjugate. W (z) = 0 can also be expressed as Equation 43 as shown below: Equation 43: W (z) = 0 = (z-r〇) (z-Γι) ... (ζ _ ~ 2) (ζ _ Γμ ι), the μ process then proceeds to block 668, where additional restrictions on the root are added so that | can be used in FIR or IIR processing mode and remains stable. For example, if w is used for implementation, no restriction is required to ensure stability, but if used for nR implementation, the root must be within the unit circle. The unit circle refers to the inner circle of the plane defined by the X axis corresponding to the real quantity and the y axis corresponding to the virtual quantity. Draw the circle and the origin of the element (the intersection of the X and y axes, corresponding to 〇 on the X and y axes), and the radius is 丨. For 86017 • 94- 200417167, the root rk of each W (z) that is not located in the circle of the unit is transposed so that it lies in the circle of the unit. Alternatively, the root is not required to be within the circle of the unit, and the addition can be limited so that the W (z) root is within a circle with a radius of p centered at the origin, where p is less than ^. Therefore, in this specific embodiment, for each W (zM ^ rk that is not located in a circle of radius p, the root rk is transposed so that it is located in the circle of a unit of radius p. Therefore, if W (ie, the value of rk) is greater than p, the transposition can be expressed as the formula: Formula 44: Ή where P < 1 Pay attention to the above formula < The plural common rails shown. The flow then proceeds to block 670, where the new filter coefficient Wnew # is constructed from the modified root. That is, if any root must be modified due to the restriction of adding block 668, the modified root can be used to determine a new root which can be substituted into the above formula r0, ..., rM1 to provide a new w (z) (ie wnew). wnew is then used as the filter coefficients for the remainder of the flow, which continues to block 650 of Figure 30). Therefore, FIG. 32 can be used to restrict or project the root of w. FIG. 36 illustrates a specific embodiment of a method for offline design of filter coefficients w in tandem group impulse response estimation. The flow begins at block 7 () 4: which; for all design methods, each line is pulsed in series and the answer W should be determined. Therefore, if two design methods are used for the eight channel pulses in the tandem group: Yes, a total of 16 answers (hip to trade) are determined. In addition, the method of figure% is not limited to any decision method. The flow then proceeds to the block, where the convolution Bk of each W-estimated W ′ is estimated, and the impulse response of each channel in the tandem group. Therefore, in the current example, there are 16 answers (w. To ~) and 8 tandem group responses, and a total of 128 convolutions are estimated. For any answer w, each Bk can therefore be expressed as Bk = [bk〇, bk "^^ 86017 -95- 200417167 bk, i, ..., bk, Ni are coefficients and N is the length of Bk. It should be noted that [k] 8 represents the number of channels in the tandem group. The flow proceeds to block 708, where, for each Bk, a dispersion region with the required length of the maximum energy (ie, the length of the target filter) is located. That is, the maximum energy dispersion region can use formulas 40 and 41 Positioning, where LW corresponds to the length of the scattered area. Therefore, the formula of block 708 can be expressed as follows. Formula 45: as far as possible, n | where η = 0, 1, ..., n-1 / = 0 The above formula is the same as the above Equation 40 is similar. In Equation 45, N is the length of a specific Bk and Ek (n) corresponds to the energy in the sliding window LW (corresponding to the required length of the dispersed area). Therefore, the energy value of N (Ek (0),. &Quot;, Ek (N-1)) is determined for each heart. Therefore, the dispersion region with the maximum energy for each Bk can be determined using the following formula. EQUATION 46: Dk = arg (max Ek (n))-LW where η = 〇, 1, ..., N-1 Formula 46 is similar to the above formula 41. In Formula 46, Dk corresponds to the purity of the maximum energy Bk. Delay, so the dispersed area starts with Dk and ends with Dk + LW and has the maximum energy. (Alternatively, the end of the dispersed area can be defined as the point where the energy Ek (n) falls below a predetermined limit, as in the above reference formula 41.) Therefore, for each Bk, the maximum energy dispersion area is determined. The flow then proceeds to block 710, where for each Bk, the estimated merit value pMk. The merit value is defined as the maximum energy, the maximum Ek (n) (from the block 708) and the total energy Ek of Bk. Therefore, Ek can be defined using the following formula: Equation 47: / = 0 In the above formula, N refers to the length of Bk. The figure of merit can therefore be expressed as follows. 86017 -96- 200417167 Equation 48 : Where n = 〇1, N-1 In the above formula, N refers to the length of Bk. The flow proceeds to block 712, where the average figure of merit fmavg used for all channel impulse responses is determined. Each answer wk (k = 0 in the above example) 1, 1, 15) will have its own mean figure of merit fmavg. The flow proceeds to block 714, where the selective filter w is selected so that FMavg is maximized among all possible design methods. The method of Figure 36 can be taken offline Implementation, and The final selection W can be stored in the echo canceller 20 and loaded into the non-adaptive filter 64, 31, or 35 as needed. Response Note Although the above description of W assumes that the additional filter is non-adaptive, adaptive filters are available To replace the non-adaptive filters 64, 31, or 35. However, 'to ensure stability as described above, W needs to add restrictions, if the filter is adaptive, the stability restrictions may need to be added on a per sample basis, Because filter adaptation can lead to unstable filters. 33 to 35 provide a possible design method for determining W. For example, FIGS. 33 to 35 may correspond to the three design methods used in block 704 of FIG. 36. Alternatively, any or all of the methods of Figs. 33 to 35 may be performed by the echo canceller 20 in operation. In a specific embodiment, the echo canceller 20 can perform all three design methods to determine W (e.g., block 664 in FIG. 32) and finally (after block 632 in FIG. 28) decide which one provides the best solution. It should be noted that different methods can provide the best solution after modifying the roots of the best solution after the reconstruction of the new filter coefficients from the modified root (block 668 in Figure 32). Therefore, different methods can be analyzed or selected by the echo canceller 20 at different times according to specific embodiments. Alternatively, a 'single design method may be used by echo cancellation 86017 -97- 200417167. Fig. 33 is a part of block 664 of Fig. 32, which corresponds to a method for determining new filter coefficients according to a specific embodiment of the present invention. The flow starts at block 6 7 2 ′ where the scattered area with adaptive filtering of 6 2 coefficients is moved to the ring buffer to compensate for the delay D (calculated in block 644). Therefore, the delay compensation coefficient G can be expressed as G = [g0, gi, ..., gN-i], where g0, gl, ..., gN is the coefficient of the delay compensation coefficient G, and N is the length of the adaptive filter 62. Therefore, the relationship between 'H (corresponding to the uncompensated coefficient of the adaptive filter 62 of the original snapshot used in box 640 in FIG. 29) and G can be expressed as follows. Formula 49 · • F = A [dwL where i = 〇 , L, · η, N-1 In formula 49, gi is a delay compensation coefficient, which is stored in the corresponding position i of the ring buffer. Fig. 3 8 illustrates a specific embodiment of the exemplary impulse response of Fig. 37 , Which has been compensated by the time delay D (corresponding to T1 in Fig. 37), so that the impulse response starts from the dispersion time (defined by T4 + T2 in Fig. 37). Therefore, the chirp coefficient represents the impulse response in Fig. 37 and the G coefficient represents The time-compensated impulse response in FIG. 38. The process proceeds to block 674, where the required filter length is defined. For example, in a specific embodiment, as described above, the required filter length is defined as 10 (where the impulse response dispersion time requires Compressed into 10 samples). Refer to the example in Figure 38 'Required The waver length corresponds to the time between D0, 0, and 31, where 0 is the beginning of the dispersion time (also corresponds to (the first coefficient of 3, because G has delayed compensation)) and S1 defines the end of the required filter length. Once the required filter length is determined, the coefficients of the scattered area within the required filter length are cleared to define the residual coefficient, V. That is, gi is set to 0, where 1 = 0, 1,..., S1. Therefore, v may be Expressed as v = [〇, ··, 〇, v ,, ..., 86017 -98- 200417167 vK_i], where K is the number of V non-zero components, and each V non-zero coefficient is defined as follows. Formula 50: Vj = gsi + j where j = 0, 1, .., κ-1 Therefore, referring to the example in FIG. 38, the G coefficient corresponding to time T5 is set to zero, and the V coefficient represents residual distortion, that is, the impulse response part corresponding to time T6. The flow proceeds to blocks 676 to 680, and its operation is to equalize the residual distortion, V. In block 676, the fast Fourier transform (FFT) of V is calculated. The flow proceeds to block 678, where the inverse I of FFT (V) is calculated, Where I = 1 / FFT (V). The flow proceeds to block 680, where Wi is used as I Inverse FFT (IFFT) calculation, where W ^ IFFTCI). Therefore, the inversion of Wj V can be used to equalize residual distortion V. The flow proceeds to block 682, where W is determined from a predetermined length W! Window with the maximum energy (and The estimated channel dispersion in Figure 29 is the same), where the predetermined length is the required number of adaptive filter taps. The flow then proceeds to block 684, where the filter coefficient W is normalized, for example, by the Euclidean number of W ( Ie L2 standard). Fig. 34 is an alternative method for determining the coefficient W according to a specific embodiment of the present invention. The flow begins at block 686, as described above, where the filter length required to use the delay compensation filter coefficient G (from block 672 and equation 49) is defined, and the convolution matrix C is determined. The convolution matrix C is defined as follows.

Equation 51 ·· C = SL · ConG SL corresponds to the selection matrix, which can be expressed as follows. Formula 52: SL = [0; /] In Formula 52, 0 is a (N + Μ-L-1) xL zero matrix, and I is a (N + ML-1) x (N + Μ-L-1) Identity matrix. In this formula, N corresponds to G length, 86017 -99- 200417167 M corresponds to W length (predefined number of non-adaptive filter taps to complete the required adaptive filter length), and L corresponds to the required adaptive filter length. ConG corresponds to a G convolution matrix and can be expressed as follows. Formula 53

ConG = g2 0 g〇 0 Si g〇 * · • 9 * · ο ο οοο

The Sn-\-(N + M-\) χM matrix C is therefore a convolution matrix ConG derived from the delay compensation filter coefficient G by ignoring its initial l columns, which will define the shortened channel coefficients at the end of this design method.

The flow proceeds to block 688, where bw is defined as the first column of the matrix C in Equation 51 placed in a column vector. Flow proceeds to block 690, where matrix a is calculated. Equation 54: A = CTC In Equation 54, the sign CT refers to the transposition of matrix C. The flow proceeds to block 692, where Aw = 解 given formula system solution W. The flow then proceeds to block 694, where the filter coefficients W are normalized. Those skilled in the art will immediately recognize that the above-mentioned answer corresponds to the system CW = [1, 0, ...] τ minimum mean square error answer, and it attempts to treat the overall convolution of the adaptive filter 62 coefficients as W equalization distortion.

Fig. 35 is an alternative method for determining the coefficient w according to another embodiment of the present invention. The flow begins at block 696, as described above, where the filter length required to use the delay compensation filter coefficient G (from block 672 and equation 49) is defined, and the convolution matrix C is determined. Therefore, Fig. 35 uses the same C matrix as defined by Equation 51. The flow then proceeds to block 698, where the matrix A is calculated as shown in block 690 of Figure 34 (see equation 54). The process proceeds to block 700, where the largest answer of WWTW / WTAW 86017-100 · 200417167 is estimated. It should be noted that the energy ratio of W normalized taps weighted by matrix A is provided, so the largest answer of WTW / WTAW minimizes the energy WTAW (its condition is WTW = 1) to a minimum. It should be noted that WTW / WTAW is equal to WTW / WTAW, where I is the identity matrix, so that the answer W is the generalized eigenvector corresponding to the largest (I, A) eigenvalue of the pair, and it can use any ready-made algorithm for estimating the eigenvector Method calculation. Flow then proceeds to block 702, where the filter coefficients W are normalized at block 700. Therefore, Figures 33 to 35 provide three design methods that can be used to determine the filter coefficient W. It should be noted that the method of Fig. 33 attempts to equalize the residual distortion beyond the length of the (target) filter. The method of Fig. 34 is more integrated because it attempts to equalize the distortion while considering the overall convolution of the adaptive filter 62 coefficients as W. However, Figure 34 produces more complex formulas. The method of FIG. 35 attempts to minimize the residual distortion energy after the convolution of the adaptive filter 62 coefficients and W. As described above, the echo canceller 20 can implement all methods, and the best answer is to choose before modifying the W root or after reconstructing a new filter coefficient W from the modified root. In the foregoing specification, the invention has been described with reference to specific embodiments. However, those skilled in the art should understand that the present invention may have various modifications and changes without departing from the scope of the present invention as set forth in the following patent application scope. For example, any of the methods described in this article can be embodied as software on one or more computer hard disks, floppy disks, 3.5mm disks, computer storage tapes, magnetic drums, static random access memory (SRAM) units, Dynamic random access memory (DRAM) unit, electrically erasable (EEPROM, EPROM, flash) unit, non-volatile unit, ferroelectric or ferromagnetic memory, compact disc (CD), laser disc, optical disc And any similar computer-readable media. Similarly, the block diagram may not be 86017 -101- 200417167. The blocks described may have more or fewer blocks or be configured differently. Similarly, the flowchart may be configured differently, including More or fewer steps that are configured differently, or the steps that they have can be divided into multiple steps or steps that can be performed simultaneously with each other. Therefore, the description and the drawings should be regarded as illustrations, not as limitations, and All such modifications are within the scope of the present invention. The advantages, other advantages, and solutions to problems of specific embodiments have been described above. However, any advantages, advantages, or advantages of solutions are produced or highlighted , Problem solutions, and any elements should not be considered as key, necessary or basic functions or elements of any or all patented scopes. The terms "including", "including" or any other variations thereof "used herein It is used to cover non-proprietary inclusions so that process methods or devices that include a list of components include not only those components, but also explicitly listed or original procedures, methods, articles, or devices [Schematic description of the figures] The present invention will be illustrated by examples in these examples and the contents of the drawings, and among them: and the drawings to explain, but the present invention is not limited, where similar reference symbols represent similar meta-graphs 1 is according to the present invention— FIG. 2 is according to the present invention—a sound canceller; a communication system according to a specific embodiment; FIG. Is a diagram of a specific embodiment. Figure 4 is a -102- 200417167 adaptive filter of the echo canceller of Fig. 2 according to a specific embodiment of the echo canceller of Fig. 2 according to a specific embodiment of the invention. Figure 5 is a non-linear processor of the echo canceller of Figure 2 according to a specific embodiment of the present invention;% 'Figures 6 to 8 are monitoring and control units of the echo canceller of Figure 2 according to various specific embodiments of the present invention Part; 'FIG. 9 is a flowchart of the operation of the echo canceller of FIG. 2 according to a specific embodiment of the present invention; FIGS. 10 to 13 are the proximal ends of the echo canceller of FIG. 2 according to a specific embodiment of the present invention Flow chart of a signal detector operation and a method for backing up and restoring the filter coefficients of the adaptive filter of the echo canceller of FIG. 2; FIG. 14 is a diagram for monitoring the gain of the echo canceller of FIG. 2 according to a specific embodiment of the present invention; Flow chart of a dynamic gain control method; FIG. 15 is a flow chart of a method for monitoring an echo canceller adaptive filter of FIG. 2 and a filter coefficient distribution state of a wave filter coefficient according to a specific embodiment of the present invention; FIG. 16 to 19 are flowcharts of the operation of the non-linear processor in the echo canceller of FIG. 2 according to a specific embodiment of the present invention; FIGS. 20 to 24 are pure delay estimates and Flow chart of sparse window position; Figures 25 to 27 are flowcharts of an audio detection method according to a specific embodiment of the present invention; Figures 28 to 36 are methods of shortening echo path selection according to a specific embodiment of the present invention Flow charts; and FIGS. 37 to 38 are examples of impulse response examples according to the specific embodiment of the present invention. 86017-103 · 200417167 Those skilled in the art may find that, for simplicity and clarity, the elements in the drawings have not been drawn to scale. For example, in order to help understand the specific embodiment of the present invention, the dimensions of some elements in the figure may be excessively enlarged compared to other elements. [Illustration of Representative Symbols] 10 Communication System 12 Transmitter / Receiver 13 Interface 14 Transmitter / Receiver 15 Interface 16 Union 17 Control 18 Union 20 Echo Canceller 22 Echo Canceller 24 Communication Network 26 Near-end signal detection 28 Adaptive filter unit 30 Monitoring and control unit 31 Non-adaptive filter 32 Non-linear processor 33 Gain control adder 34 86017 -104- 200417167 35 Non-adaptive filter 36 Adder 37 Transmission input 38 Transmission input 39 Transmit input 40 Receive output 41 Serial signal 42 Transmit output 43 Receive input 44 Receive input 45 DC notch wave filter 46 Error signal 47 Error signal 48 Echo estimation signal 49 DC notch wave filter 50 Near-end signal level estimator 52 Far-end signal level estimator 54 Transmit input signal level estimator 56 Background processor 58 Near-end signal detection limit 59 Selector 60 Near-end signal detector 62 Adaptive filter 64 Non-adaptive filter- 105- 86017 200417167 66 Delay 68 Signal level estimator 70 Near-end signal level estimation 72 Remote signal level estimator 74 Non-linear processor controller 76 Non-linear processor enable controller 78 Non-linear processor shutdown controller 82 Noise level matcher 84 Output signal mixer 86 Suitable noise generator 88 Short-term background level estimator 90 Background level estimator controller 92 Long-term background level estimator 94 Background level adapter 96 Adaptive background level estimator 100 Gain monitor 102 Filter coefficient monitor 104 Decimal filtering Selector 106 Decimal Sampler 108 Decimal Filter 110 Decimal Sampler 112 Comparator 114 Near-End Signal Detector 116 Echo Return Loss Enhancement Estimator 86017-106- 200417167 118 Power Estimator 120 Power Estimator 122 Adaptability Filter 124 Maximum locator 126 Delay determination 128 Adaptive filter system 130 Estimated delay 132 Noise generator 134 Power estimator 136 Delay 138 Delay 140 Multiplier 142 Multiplier 144 Adder 146 Value 148 Low-pass filter 150 Storage 152 Smooth Correlator 154 Delay 156 Multiplier 158 Implementer 160 Low-pass filter 162 Low-pass filter 164 Oscillator 86017-107- 200417167 166 Audio instruction determination unit 168 Audio indicator signal 200 Flow chart 202 Start 204 ^ 206 ^ 208-209 Block 210 Monitoring and control adaptability 漉Wave 211, 212, 213, block 214, 216, 218 220 decide diamond 222 block 224 decide diamond 226 > 228-230 block 232 decide diamond 234 end 252 block 254 decide diamond 256, 258 block 260 decide diamond 262 '264 block 266 decision diamond 268 > 270 box 276 box 278 > 280 > 282 decision diamond 284, 286, 288, box 86017-108- 200417167 290 293 296 300 304 312 322 326 '332 334 336 33 8 8 340 342 344, 348 350 354 356 360 362 400 406 412 291 > 292 294 > 295 decide diamond 298 block 302 decide diamond 306, 308 '310 block 314, 316, 318 status 324 decide diamond 328, 330 block determine diamond block determine diamond block determine diamond Block 346 Block decision diamond 352 Block decision diamond 358 Block decision diamond 402-404 Decision diamond 408, 410, Block 414 -109- 86017 200417167 416 418 424 426 430 432 440 448 450 452 456 458 466 470 476 482 485 486 491 494 497 500 504 505 Decision diamond 420 > 422 block determines diamond 428 block determines diamond 434, 436, 438 block 442-444 ^ 446 determines diamond block determines diamond 454 block determines diamond 460 > 462-464 block 468 determines diamond 472 > 474 > block 478 > 480 483, 484 decide the diamond 489 block 487 > 490 decide the diamond 492 > 493 > block 495 > 496 498 > 499 decide the diamond 501, 502, 503 block determine the diamond 506 block 86017 • 110- 200417167 508 decide the diamond 510 box 512 determines diamond 514, 516, 518 box 520 determines diamond 521, 522 box 523 determines diamond 524 box 528, 529 determines diamond 530, 532 box 534, 535 determines diamond 536 > 538 box 540 determines diamond 542, 544, 550 , 552 party 554 decision diamond 556, 558 square 560 > 562 decision diamond 564 > 566 > 568 square 570 decision diamond 572 square 574 decision diamond 576 square 578 > 580 decision diamond 582, 584, 586 '588 square 86017-ill-200417167 590 decide diamond 592 block 594 decide diamond 596 > 598 block 600 decide diamond 602, 604 '606' 608 block 610 decide diamond 612 block 614 decide diamond 616, 618 block 620 decide diamond 622 > 624 block 626 > 628 decide diamond 630, 632, block 634, diamond 636, 638 > 640 '642 > 644, 646' 648, 650, 652, 654, 656, 658, 660, block 662, diamond 664 '666' 668, 670 > 672-674 > 676-678-680 > box 86017 -112- 200417167 682 688 694 700 706 712 684 690 696 702 708 714 686, 692 > 698 > 704, 710, 86017 -113-

Claims (1)

200417167 Patent application park: 1. A method for performing non-linear processing of audio signals for selectively affecting background noise, including: selectively estimating the background noise to generate—estimate background noise Level; determining whether the estimated background noise level exceeds a predetermined standard; and selectively injecting the predetermined background noise into the audio signal. 2 · An echo canceller, comprising: an adaptive filter; a control circuit coupled to the adaptive filter; and a non-linear processor integrated to the control circuit to selectively estimate the moon scene noise Signal, and if one of at least one near-end signal and one far-end signal is detected to interrupt background noise estimation. 3. A method for performing near-end signal detection, including: determining the power of a 'receiving path' output; determining the power of an error signal; and using the power of the output of the receiving path control And the power of the error signal detects whether the near-end signal is present. 4. An echo canceller, comprising: a near-end 彳 & detection as' it determines a power output from one of a receiving path and a power of an error signal to detect whether a near-end signal exists; An adaptive filter coupled to the near-end signal detector; a control circuit coupled to the adaptive filter; and a non-linear processor coupled to the control circuit. 86017 5. A method for performing echo cancellation, comprising: performing adaptive filtering for the purpose of echo cancellation; estimating pure delay simultaneously with the step of performing adaptive filtering; and selectively using the pure delay to adjust A position of an adaptive filter window. 6. A method for performing echo cancellation, comprising: determining separately estimated delay values; and performing non-linear filtering of the individually estimated delay values to produce a pure delay; and selectively using the pure delay to adjust an adaptive filtering Position of the filter window; and perform adaptive filtering for this purpose of echo cancellation. 7. · An echo canceller, comprising: a first adaptive filter for cancelling an echo; and a monitoring and control unit rounded to one of the first adaptive filters, including a second adaptive filter The monitoring and control unit for estimating pure delay, wherein the pure delay is used to adjust a position of an adaptive filter window of the first adaptive filter, and wherein the second adaptive filter includes The filtering estimates the delay value separately to produce one of the pure delays, a non-linear filter. 8. A method for indicating whether an audio signal exists in a communication signal, including: delaying the communication signal by a predetermined first delay to generate a first-delay signal; 86017 -2- 200417167 by a predetermined first Two delays delay the communication signal to generate a second delay signal; combine the first delay signal, the first delay k number, and the communication signal to generate an estimated signal; and use the estimated signal to indicate whether the audio is present. 9. An audio detector for detecting an audio in a communication signal includes: a first circuit for providing a first delay signal; a second circuit for providing a second delay signal; An estimation circuit that combines the first delayed signal, the second delayed signal, and the communication signal to generate an estimated signal; and an audio indicator for using the estimated signal to indicate whether the audio is present. I 0 · A method for reducing the dispersion of a wave filter with a plurality of filter coefficients, the method includes: when a near-end number, a parameter is below a predetermined limit, and a distance When a parameter of the terminal signal is below a second predetermined limit, the adjustment of at least a part of the plurality of filter coefficients is interrupted. II · A method for reducing the divergence of a filter having a plurality of filter coefficients, wherein the filter has a plurality of states including a first state and a second state, the method includes: determining the adaptability One of the current states of the filter; storing the plurality of filter coefficients when the current state of the adaptive filter is the first state; and 86017 200417167 when the current state of the adaptive filter is the second state The storage of the plurality of filter coefficients is not performed at the time. 12. A method for improving the stability of an adaptive filter and a wave filter used in a communication system, the method comprising: selectively adjusting a gain of an error signal; and selectively adjusting the gain of the error signal The gain responds to ensure that one of the pass and the system gain is not greater than one. 13. A method for indicating whether an audio signal exists in a communication signal, comprising: delaying the communication signal by a predetermined delay to generate a delayed signal; generating a generated sinusoidal signal; combining the delayed signal and the generated sinusoidal signal And the communication signal to generate a first correlation signal and a second correlation signal; and using the first correlation signal and the second correlation signal to indicate whether the audio has been detected. 14. A filter for filtering a signal, comprising: a first adaptive filter for filtering the signal at a first adaptive rate; and a first adaptive filter for synthesizing to the first adaptive filter for the first adaptive rate. Two adaptive rate filters One of the signals is a second adaptive filter, wherein the first adaptive rate is less than the second adaptive rate. 15. A method for performing adaptive filtering of a signal, comprising: filtering the signal at a first adaptive rate to produce a first filtered output; and 86017 -4- 200417167 adaptive adaptation of the signal to the wave rate. The first filtered output generates a one-by-one filtered output, wherein the first adaptive rate is less than the second adaptive rate. 16. A method for improving the stability of an adaptive filter, comprising: monitoring a plurality of filter coefficients in the adaptive filter; detecting a predetermined pattern in the plurality of filter coefficients; and if After detecting the predetermined pattern, different adaptive filter coefficients are loaded into the adaptive filter. 86017