EP4367900A1 - Procédé assisté par ordinateur pour le traitement stable d'un signal audio à l'aide d'un algorithme lms adapté - Google Patents

Procédé assisté par ordinateur pour le traitement stable d'un signal audio à l'aide d'un algorithme lms adapté

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Publication number
EP4367900A1
EP4367900A1 EP22746970.7A EP22746970A EP4367900A1 EP 4367900 A1 EP4367900 A1 EP 4367900A1 EP 22746970 A EP22746970 A EP 22746970A EP 4367900 A1 EP4367900 A1 EP 4367900A1
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EP
European Patent Office
Prior art keywords
filter
audio signal
computer
algorithm
input
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22746970.7A
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German (de)
English (en)
Inventor
Ludwig KOLLENZ
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Austrian Audio GmbH
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Austrian Audio GmbH
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Filing date
Publication date
Application filed by Austrian Audio GmbH filed Critical Austrian Audio GmbH
Publication of EP4367900A1 publication Critical patent/EP4367900A1/fr
Pending legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/02—Circuits for transducers, loudspeakers or microphones for preventing acoustic reaction, i.e. acoustic oscillatory feedback
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
    • G10K11/17821—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
    • G10K11/17825—Error signals
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
    • G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1787—General system configurations
    • G10K11/17875—General system configurations using an error signal without a reference signal, e.g. pure feedback
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/10—Applications
    • G10K2210/108—Communication systems, e.g. where useful sound is kept and noise is cancelled
    • G10K2210/1081—Earphones, e.g. for telephones, ear protectors or headsets
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3056—Variable gain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/10—Earpieces; Attachments therefor ; Earphones; Monophonic headphones
    • H04R1/1083—Reduction of ambient noise
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2460/00—Details of hearing devices, i.e. of ear- or headphones covered by H04R1/10 or H04R5/033 but not provided for in any of their subgroups, or of hearing aids covered by H04R25/00 but not provided for in any of its subgroups
    • H04R2460/01—Hearing devices using active noise cancellation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R25/00—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception
    • H04R25/45—Prevention of acoustic reaction, i.e. acoustic oscillatory feedback
    • H04R25/453—Prevention of acoustic reaction, i.e. acoustic oscillatory feedback electronically

Definitions

  • the invention relates to a computer-aided method for processing an audio signal using an adapted LMS algorithm according to the preamble of claim 1.
  • LMS algorithms the collective term for algorithms in which the squares of error are minimized (least mean squares) have long been used in electroacoustics, in particular for adaptive filters in numerous variants and for the most diverse areas of application.
  • the currently most used variants are known by the following abbreviations: LMS, NLMS, sign-LMS, LMS with variable increment (VSS-LMS), LMS with correlation factor or NLMS and for special applications variants such as filtered-x LMS or filtered-e LMS ( see International Journal of Electrical and Computer Engineering Vol 7, No 5, October 2017, LMS Adaptive Filters for Noise Cancellation: A Review, pp 2520ff).
  • LMS LMS algorithms
  • sign-LMS can be used in this context.
  • US 2008/0063230 shows the use of a classic LMS algorithm in conjunction with an Adaptive Digital Filter (ADF).
  • ADF Adaptive Digital Filter
  • US 2017/0201276 shows an LMS application (standard system estimation) in which the input signals of the LMS algorithm are delayed differently.
  • the NPL document "Design the adaptive noise canceller based on an improved LMS algorithm and realize it by DSP” by Xu Yanhong and Zhang Ze shows the possibility of using an improved sign-LMS as an adaptive system estimator.
  • a disadvantage of all solutions is the potential instability of the system. Exponential smoothing uses the same coefficient for the attack and release cases. Due to this property, the filter coefficients of adaptive filters can converge towards infinitely high values under certain unfavorable conditions. Such overshooting of the filter coefficients is prevented by the word length of the processor used. This property is called clipping.
  • a typical case of such clipping is acoustic feedback suppression: if feedback occurs (the microphone records the loudspeaker), this can be heard as a very loud sine wave (“howling”).
  • an adaptive filter is configured to filter the speaker output and subtract at the microphone. In the best case, it filters the feedback sine exactly and subtracts it from the microphone, ergo the sine is eliminated.
  • the adaptive filter tries to eliminate it. Since it does not get weaker despite subtraction (the source is no longer the loudspeaker but external), the filter coefficients swell towards infinity. The result is a distorted sound. In order to avoid this erroneous convergence, it is known in the prior art to monitor the coefficients using an external algorithm and to limit them at a specific threshold value, which is useful in terms of the result, but entails greater effort in terms of complexity and computing power.
  • a problem occurs, for example, with hearing systems located in the ear, such as in-ear headphones or hearing aids, when the device is removed from the ear. By eliminating passive damping, a method that uses a classic LMS algorithm tries to increase the gain of the ANC filter to infinity, since there is no longer any ANC power. Thus, additional sensors must be used to detect whether the device is in the ear or not.
  • a new PEAK-LMS algorithm which has the features specified in the characterizing part of claim 1, in other words, in which a signum function is calculated from an input signal sequence and a filter output sequence, which together with a sequence of initial filter values and a sequence of time constants for the attack and the release case, which do not have to be the same, are used to calculate a sequence of adapted filter values.
  • upper and lower limits are set for the coefficients in the method/algorithm itself, so that the risk of upward convergence is intrinsically excluded, which also eliminates the need for complex, external monitoring.
  • Fig. 1 shows the schematic structure of a feedback suppression of an acoustic system
  • Fig. 2 shows stable values for the coefficients of the filter, which were obtained according to the method described in Fig. 1 using the PEAK-LMS algorithm
  • Fig. 3 filter coefficients of an LMS algorithm for a system with sinusoidal outer tones
  • 4 filter coefficients of a PEAK-LMS algorithm for a system with sinusoidal external tones
  • FIG. 5 the application of the invention to the control of an amplifier
  • FIG known form is used
  • Fig. 7 the unit step response of the filter for the attack case
  • Fig. 8 the unit step response of the filter for the attack and the release case
  • Figs. 9a and 9b in two further variants the adaptation speed and the
  • FIG. 10 the convergence of a filter coefficient of an advantageous embodiment of the invention, in which this method is also applied to the adaptation speed
  • FIG 12 shows an example of an echo suppression application
  • FIG. 13 shows an example of an adaptive ANC application
  • FIG. 14 shows a variant of an adaptive ANC application
  • FIG. 15 by way of example a variant of the application of an adaptive equalizer
  • FIG. 16 by way of example a second variant of the application of an adaptive equalizer.
  • the microphone 1 shows the schematic structure of feedback suppression of an acoustic system, as is known from the prior art, but can also be used for the method according to the invention.
  • the microphone 1 records the output of the driver (loudspeaker 2), resulting in a so-called acoustic feedback 5 in the form of the feedback path h(n). In the unfiltered case, this can be audible as a very loud sine wave.
  • the function block for adapting the filter 4 (adaptation block for short) calculates filter coefficients for an adaptive filter 3 from the reference signal d(n) (reproduction) and the error signal e(n) (result of the subtraction), which are then used as the filter output y(n ) are issued.
  • the adaptation block 4 thus attempts the transfer function of the feedback path 5 estimate and apply by means of adaptive filter 3, estimate.
  • the filtered output of the driver is subtracted at the circuit's microphone input (downstream of the physical microphone). If the transfer function is correctly estimated, the filter “clips” the sine and subtracts it from itself at the input, thereby suppressing feedback.
  • LMS, SignLMS and PEAK-LMS are entered in adaptation block 4 in FIG. These represent variants for the adaptation algorithm. These three variants are compared below under identical conditions:
  • LMS is a gradient method, where the gradient is based on the least squares error.
  • the learning rate thus defines an increment by which the coefficients of the LMS algorithm are adapted.
  • the LMS algorithm does not produce a valid result for the filter coefficients because the adaptation is too slow for a learning rate of 0.00005.
  • a Sign-LMS algorithm shows a similar behavior and becomes unstable for a too high learning rate, while it adapts too slowly for a too low learning rate. For this example, a learning rate of 0.00001 is close to the optimum for an LMS or Sign-LMS algorithm.
  • the PEAK-LMS algorithm shows a significantly higher tolerance: for 0.00005 the suppression performance is reduced, but the algorithm remains with stable values for the coefficients.
  • FIG. 2 shows values for coefficients for a filter function c(n), which are calculated according to the method described in FIG. 1 using the PEAK-LMS algorithm at a learning rate of 0.00005 were obtained for a FIR filter. It can be clearly seen that the values of the coefficients remain stable.
  • the abscissa shows the number of the filter coefficient and the ordinate shows the value of the coefficient.
  • FIG. 3 shows an example of the filter coefficients of a prior art LMS algorithm for a system with sinusoidal external tones.
  • the abscissa shows the number of the filter coefficient and the ordinate shows the value of the coefficient.
  • a typical problem for feedback suppression is the case of an "external" sinusoidal tone, i.e. one not brought into the system by feedback.
  • the adaptive filter recognizes the sine tones and wants to suppress them, since they are mistakenly regarded as feedback.
  • the problem arises with the adaptive filter that the supposed feedback does not become weaker due to the subtraction at the microphone and the filter gets into an incorrect setting (this effect is called "entrainment").
  • Fig. 4 shows an example of the filter coefficients for a filter function c(n), determined using the PEAK-LMS algorithm according to the invention, for a system with sinusoidal outer tones, identical to that in Fig. 3.
  • the abscissa shows the number of the filter coefficient and the ordinate the value of the coefficient.
  • PEAK-LMS prevents the coefficients from rising too far.
  • the PEAK-LMS algorithm would limit the filter coefficients to ⁇ 1, but in the case shown it generally prevents an incorrect setting. It is also immediately apparent that a method according to the invention that is based on a PEAK LMS algorithm can work more efficiently than one that works, for example, with a classic NLMS algorithm, resulting in savings in the required computing power that either for others processes can be used, or can contribute to an increase in battery life.
  • FIG. 5 shows the regulation of an amplifier based on the estimation of the gain of a transfer function.
  • the gain of a transfer function must be controlled in real time.
  • real-time means with the lowest possible latency and time-invariant, i.e. the processing cannot take place independently of the temporal signal.
  • the processing of an audio file that has already been recorded would therefore not be real-time, since a calculation is theoretically not subject to any time pressure.
  • incoming microphone data must be processed as soon as possible.
  • the amplification of the primary path (outer microphone) and/or the amplification of the secondary path (inner microphone) must be adapted in order to optimize the performance of the ANC system.
  • the method according to the invention can be used not only in ANC systems, but wherever adaptive filters are used, for example in echo suppression, feedback suppression, system estimation ( Recognition of an unknown transfer function), channel equalization (also applies to HF technology), adaptive inverse control, hum suppression (50Hz hum of the power supply filter for e.g. ECG sensors), vector voltmeter, separation of signals with different correlations, interference suppression in audiological measurement systems (e.g. measurement of otoacoustic emissions) and many others.
  • adaptive filters for example in echo suppression, feedback suppression, system estimation ( Recognition of an unknown transfer function), channel equalization (also applies to HF technology), adaptive inverse control, hum suppression (50Hz hum of the power supply filter for e.g. ECG sensors), vector voltmeter, separation of signals with different correlations, interference suppression in audiological measurement systems (e.g. measurement of otoacoustic emissions) and many others.
  • FIG. 6 shows a block diagram of an example using an LMS algorithm in its known form.
  • x(n) describes the input signal
  • h(n) describes the transfer function to be estimated (transfer function of the acoustic system)
  • y(n) describes the output of the filter
  • c(n) describes the coefficients of the adaptive filter
  • d(ri) describes the reference signal for the LMS algorithm
  • e(n) the error signal which follows from the difference between the value y obtained and the predetermined value x, in each case in the nth step.
  • FIG. 6 shows the LMS block that determines the necessary adaptations of the filter c(n).
  • x(n) is the element of vector x(n) at time n.
  • c(n+1) stands for the result of the nth adaptation of c, c(n) for the value of c obtained in the nlth step; m (also mu) for the adaptation rate; e(n) for the error signal in step n, x(n) for the input signal sequence representing the input data of the audio signal up to time n and y(n) for the output of the filter in the nth step.
  • time constants a and ß stand for the attack and release case and thus determine the rise and fall time of the algorithm, a and ß are purely numerical, their temporal significance depends on the selected sampling rate. Both move in the interval [0; 1] from R
  • a filter using a sign-LMS algorithm with a PEAK filter is a one-pole, recursive filter per coefficient, which has an attack and a release time constant. If the input signal is greater than the filter output, the attack time constant is applied, otherwise the release time constant (see formulas III).
  • Figure 7 shows the unit step response for a single coefficient approximated according to the method of the invention (value towards which the filter tends for an input signal which jumps from 0 to 1 and then remains constant) of the filter.
  • the influence of the attack time constant can also be seen here.
  • the abscissa shows the approximation steps based on their discrete time axis and the ordinate shows the value of the filter coefficient.
  • each individual coefficient is no longer calculated by adding a step size, but via the step response of the PEAK filter.
  • w(n) is a signum function that represents the summary of the signum term of formulas II.
  • the inputs to the algorithm e(n) and x(n) could be filtered before being fed to the algorithm.
  • This has the advantage that the adaptation can be focused on a frequency range, usually with the help of an appropriate pre-filter (typically a high, low, bandpass filter, or a combination of these).
  • the desired frequency range depends on the application and can be defined individually by a specialist according to the requirements. Due to the exponential function of the PEAK filter, the LMS method can no longer converge to infinity, even if, for example, the increment (adaptation rate) m is constantly added for a coefficient due to an incorrect setting (see feedback cancellation example above).
  • Figure 8 shows the unit step response of a filter for a single coefficient, approximated according to the method of the invention using the PEAK-LMS algorithm, for the attack and release cases.
  • the abscissa shows the approximation steps based on their discrete time axis and the ordinate shows the value of the filter coefficient.
  • the factor l is fundamentally freely definable and application-related. However, the interval [0,1] makes sense for audio phrases, but [-1 ; 1] make sense.
  • L+ have values that are firmly defined in advance, which can be adapted by the specialist to the desired application.
  • the adaptation of the filter can be handled extremely flexibly, while the advantages of the sign-LMS over other variants, as well as the NLMS, continue to be retained, since no divisions are required during the calculation process, which is advantageous when converting to a fixed-point DSP.
  • An example implementation of the above is adaptive gain control.
  • An amplification should typically regulate adaptively in the range of ⁇ 6 dBFS.
  • the PEAK-LMS algorithm automatically regulates ⁇ 6 dBFS without the need for additional calculations such as converting linear values to decibels and vice versa. The elegance of the solution is thus evident.
  • the interval ⁇ 6 dBFS here is application-related for an adaptive gain control in an ANC earphone.
  • Other areas of application include feedback suppression, ANC, echo cancellation, adaptive beamforming, adaptive gain control and similar applications.
  • the space of the gain control depends on the respective product and the calculated ANC filters.
  • the sensible interval range is ⁇ 10 dBFS.
  • the volume of the pressure chamber created by the auditory canal and the in-ear headphones varies depending on the wearing situation, e.g. This in turn must be taken into account and compensated for by an adaptive ANC system.
  • FIG. 9 shows the adaptation speed and the residual error of the PEAK filter as used by the method according to the invention.
  • the abscissa shows the discrete time axis and the ordinate shows the value of the error size.
  • the sign-LMS has a residual error of convergence, the size of which depends on the step size m: A larger m leads to fast convergence, but at the same time to a larger residual error (the algorithm often "oscillates" around an optimum without it to reach); on the other hand, a smaller m leads to slower convergence with smaller residual error.
  • the variant used according to the invention shows a similar behavior.
  • the variant in FIG. 9a shows a PEAK filter with a time constant C( ⁇ , ⁇ ) of 0.001 and the variant in FIG.
  • a and b are coefficients that ensure an adaptive step size and a weighting between m(h) and e 2 (n) allow.
  • the ratio a 1 - b is chosen. Typical values here would be 1 > a > 0.8.
  • 10 shows the adaptation speed and the residual error of the PEAK filter as used in an advantageous embodiment of the method according to the invention.
  • the abscissa shows the discrete time axis and the ordinate shows the value of the error size.
  • ⁇ and ⁇ are the same but can be time-variant controlled.
  • the formulas have therefore been set in relation to time.
  • a significant advantage of this variant is the lower demand for computing capacity.
  • the improved convergence of Figure 10 over Figures 9a and b is evident from the exponential given convergence behavior. If, in the embodiment of the invention presented, different time constants are also used for this method, the formula X is obtained as an extension of formula IX:
  • step response and “step response” are used synonymously within the scope of this application.
  • an audio signal input e.g. a microphone, an external device for playing music, input of a digital interface (USB, Bluetooth, etc.
  • at least one audio signal output e.g.
  • a loudspeaker a recording device, output of a digital interface (USB, Bluetooth,..), writing a saved audio file or any other sink for an audio signal
  • a adaptive filter (3) affected data connection between the audio signal input and the audio signal output and an LMS algorithm (4) controlling the adaptive filter (3), wherein a) the LMS algorithm (4) on the basis of a reference signal d(n) and an error signal e(n) a transfer function h(ri) (this is the target value of the LMS algorithm, which can be approximated as best as possible but can never be fully reached), b) the estimate of the transfer function is applied as an adaptive filter (3) and there as one initial filter function c(n) is available (the estimation is thus transferred to the filter block and is available there as a mathematical filter function c(n) in the form of filter coefficients), c) the output of a filter output sequence as y(ri) (this is about a sequence of samples that should be as close as possible to the output sequence of h(ri)),
  • the invention is therefore a computer-assisted method for the stable processing of an input audio signal of an acoustic system, comprising at least one audio signal input, at least one audio signal output, a data connection between the audio signal input and the audio signal output which is influenced by an adaptive filter, and an LMS system controlling the adaptive filter.
  • the input signal sequence x(n) and the error signal e(n) can be subjected to a pre-filtering before being fed to the algorithm.
  • High, low or bandpass filters and combinations thereof are common here.
  • Fig. 11 shows the general case of feedback suppression ("Feedback Cancellation"; applicable to all types of headphones, hearing aids, ANC systems, etc.) analogous to Fig. E
  • a sound source is additively mixed with the sound of loudspeaker 2 and Microphone 1 of the system recorded.
  • the task of the PEAK-LMS algorithm is to estimate the acoustic feedback path H(z), to filter the loudspeaker signal accordingly and to subtract it from the microphone signal.
  • the error signal e (which is to be minimized) is the result of the subtraction and the reference d is the result for the output (usually it is taken directly from the DAC buffer).
  • M(z) denotes the transfer function of the system (e.g. equalizer, multi-band compression, etc.).
  • An example of a suitable processor is: "Onsemi Ezairo 7100" for hearing aids. This has a block floating point in an auxiliary processor.
  • FIG. 12 shows the application of echo cancellation. Similar to feedback cancellation, a PEAK-LMS algorithm has to estimate the acoustic path of the echo (H(z)). This acoustic path mixes with the speaker in front of microphone 1 and must be subtracted behind it. In contrast to feedback suppression, the microphone here does not play to loudspeaker 2, but to a receiving sink (examples: radio microphones and monitoring boxes, headsets in the field of mobile communications and intercom). The labels are analogous to Fig. 11. 13 shows the application of an adaptive ANC. In adaptive ANC, two paths can be estimated: feedforward and feedback. Depending on the system, there is only one of the two paths or both.
  • H(z) denotes the passive damping of the system and a PEAK-LMS algorithm has to estimate this path from feedforward microphone 6 and feedback microphone 7 in H(z). If this path is output inverted at loudspeaker 2, H(z) and H(z) cancel out. Another path is /(z) - this is the acoustics inside the ear cup of the ANC listener. Again, a PEAK-LMS algorithm must estimate /(z) in /(z) to achieve cancellation. /(z) in /(z) are analogous to H (z) and H(z) and are named differently because they are different paths.
  • An example of a suitable processor is: "AD AU 1860". This is a fixed-point DSP with no floating-point capability.
  • FIG. 14 shows a variant of the application of an adaptive beamforming.
  • Two microphones 1 are operated here as an endfire array, ie one signal is delayed in relation to the other and subtracted (the delay is represented by the block z ⁇ n ). This is done for two paths, with the effect of producing two opposite cardioid polar patterns as the polar pattern.
  • a PEAK-LMS algorithm now estimates the transfer function between one cardioid and the other, filters the former and subtracts its signal from the latter. The result is an adaptive beamforming where the nulls of the final directivity are controlled by the adaptive filter.
  • the speaker is not shown in this simplified schematic.
  • An example of a suitable processor is: "Onsemi BelaSigna 300". It is a fixed-point processor with a block-floating-point unit.
  • the block of a PEAK-LMS algorithm is arranged to remove noise (noise disturbance, corresponding to H(z )) from a signal. If a useful signal (source) is disturbed by additive noise, the PEAK-LMS algorithm controls a filter in the channel and equalizes the channel according to the difference in the source and the disturbed transmission. The speaker is not shown in this simplified schematic.
  • the block z ⁇ n represents a delay element.
  • FIG. 16 shows the channel equalization as a second variant of the application of an adaptive equalizer.
  • a filter /(z) is linked to a transfer function H(z) via convolution (in the time domain).

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Otolaryngology (AREA)
  • Signal Processing (AREA)
  • Filters That Use Time-Delay Elements (AREA)

Abstract

L'invention concerne un procédé assisté par ordinateur pour le traitement stable d'un signal audio d'entrée d'un système acoustique comprenant : au moins une entrée de signal audio ; au moins une sortie de signal audio ; une connexion de données entre l'entrée de signal audio et la sortie de signal audio, ladite connexion étant influencée par un filtre adaptatif ; et un algorithme LMS qui commande le filtre adaptatif ; l'algorithme de commande étant une nouvelle combinaison d'un algorithme LMS classique et d'un filtre PEAK qui assure un traitement stable du signal audio d'entrée du système acoustique.
EP22746970.7A 2021-07-05 2022-07-04 Procédé assisté par ordinateur pour le traitement stable d'un signal audio à l'aide d'un algorithme lms adapté Pending EP4367900A1 (fr)

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EP21183695.2A EP4117306A1 (fr) 2021-07-05 2021-07-05 Procédé électro-acoustique utilisant un algorithme lms
PCT/EP2022/068392 WO2023280752A1 (fr) 2021-07-05 2022-07-04 Procédé assisté par ordinateur pour le traitement stable d'un signal audio à l'aide d'un algorithme lms adapté

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EP4367900A1 true EP4367900A1 (fr) 2024-05-15

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EP22746970.7A Pending EP4367900A1 (fr) 2021-07-05 2022-07-04 Procédé assisté par ordinateur pour le traitement stable d'un signal audio à l'aide d'un algorithme lms adapté

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WO2024038216A1 (fr) 2022-09-30 2024-02-22 Austrian Audio Gmbh Système de classification pour contrôle actif du bruit

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AU6168099A (en) * 1998-09-30 2000-04-17 House Ear Institute Band-limited adaptive feedback canceller for hearing aids
US6876751B1 (en) * 1998-09-30 2005-04-05 House Ear Institute Band-limited adaptive feedback canceller for hearing aids
US9478212B1 (en) 2014-09-03 2016-10-25 Cirrus Logic, Inc. Systems and methods for use of adaptive secondary path estimate to control equalization in an audio device
US9780815B2 (en) * 2016-01-11 2017-10-03 Nxp B.V. Multi-tones narrow band RF noise elimination through adaptive algorithm

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WO2023280752A1 (fr) 2023-01-12

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