WO2001073759A1 - Ponderation spectrale perceptive de bandes de frequence pour une suppression adaptative du bruit - Google Patents

Ponderation spectrale perceptive de bandes de frequence pour une suppression adaptative du bruit Download PDF

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Publication number
WO2001073759A1
WO2001073759A1 PCT/US2001/006888 US0106888W WO0173759A1 WO 2001073759 A1 WO2001073759 A1 WO 2001073759A1 US 0106888 W US0106888 W US 0106888W WO 0173759 A1 WO0173759 A1 WO 0173759A1
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Prior art keywords
speech
power
weighting
values
signal
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PCT/US2001/006888
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English (en)
Inventor
Ravi Chandran
Bruce E. Dunne
Daniel J. Marchok
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Tellabs Operations, Inc.
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Priority to AU2001245418A priority Critical patent/AU2001245418A1/en
Priority to EP01918328A priority patent/EP1287521A4/fr
Priority to CA002401672A priority patent/CA2401672A1/fr
Publication of WO2001073759A1 publication Critical patent/WO2001073759A1/fr

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/0204—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using subband decomposition
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L21/0232—Processing in the frequency domain
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0264—Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

Definitions

  • This invention relates to communication system noise cancellation techniques, and more particularly relates to weighting calculations used in such techniques
  • the need for speech quality enhancement in single-channel speech communication systems has increased in importance especially due to the tremendous growth in cellular telephony Cellular telephones are operated often in the presence of high levels of environmental background noise, such as in moving vehicles. Such high levels of noise cause significant degradation of the speech quality at the far end receiver.
  • speech enhancement techniques may be employed to improve the quality of the received speech so as to increase customer satisfaction and encourage longer talk times
  • FIG. 1A shows an example of a typical p ⁇ or noise suppression system that uses spectral subtraction.
  • a spectral decomposition of the input noisy speech-containing signal is first performed using the Filter Bank.
  • the Filter Bank may be a bank of bandpass filters (such as in reference [1], which is identified at the end of the desc ⁇ ption of the preferred embodiments).
  • the Filter Bank decomposes the signal into separate frequency bands For each band, power measurements are performed and continuously updated over time in the noisysy Signal Power & Noise Power Estimation block.
  • SNR signal-to-noise ratio
  • the Voice Activity Detector is used to distinguish pe ⁇ ods of speech activity from pe ⁇ ods of silence
  • the noise power in each band is updated primarily during silence while the noisy signal power is tracked at all times.
  • a gain (attenuation) factor is computed based on the SNR of the band and is used to attenuate the signal in the band.
  • each frequency band of the noisy input speech signal is attenuated based on its SNR.
  • Figure IB illustrates another more sophisticated prior approach using an overall SNR level in addition to the individual SNR values to compute the gain factors for each band.
  • the overall SNR is estimated in the Overall SNR Estimation block.
  • the gain factor computations for each band are performed in the Gain Computation block.
  • the attenuation of the signals in different bands is accomplished by multiplying the signal in each band by the corresponding gain factor in the Gain Multiplication block.
  • Low SNR bands are attenuated more than the high SNR bands.
  • the amount of attenuation is also greater if the overall SNR is low.
  • the signals in the different bands are recombined into a single, clean output signal. The resulting output signal will have an improved overall perceived quality.
  • the decomposition of the input noisy speech-containing signal can also be performed using Fourier transform techniques or wavelet transform techniques.
  • Figure 2 shows the use of discrete Fourier transform techniques (shown as the Windowing & FFT block).
  • a block of input samples is transformed to the frequency domain.
  • the magnitude of the complex frequency domain elements are attenuated based on the spectral subtraction principles described earlier.
  • the phase of the complex frequency domain elements are left unchanged.
  • the complex frequency domain elements are then transformed back to the time domain via an inverse discrete Fourier transform in the IFFT block, producing the output signal.
  • wavelet transform techniques may be used for decomposing the input signal.
  • a Voice Activity Detector is part of many noise suppression systems. Generally, the power of the input signal is compared to a variable threshold level. Whenever the threshold is exceeded, speech is assumed to be present. Otherwise, the signal is assumed to contain only background noise. Such two-state voice activity detectors do not perform robustly under adverse conditions such as in cellular telephony environments. An example of a voice activity detector is described in reference [5].
  • Various implementations of noise suppression systems utilizing spectral subtraction differ mainly in the methods used for power estimation, gain factor determination, spectral decomposition of the input signal and voice activity detection. A broad overview of spectral subtraction techniques can be found in reference [3].
  • Several other approaches to speech enhancement, as well as spectral subtraction, are overviewed in reference [4].
  • Perceptual spectral weighting can improve the performance of some adaptive noise cancellation systems.
  • deficiencies in weighting functions have limited the effectiveness of known noise cancellation systems.
  • This invention addresses and provides one solution for such problems. BRIEF SUMMARY OF THE INVENTION
  • the preferred embodiment is useful in a communication system for processing a communication signal including a speech component derived from speech and a noise component derived from noise.
  • the quality of the communication signal can be enhanced by dividing the communication signal into a plurality of frequency band signals representing the communication signal in a plurality of frequency bands.
  • the dividing may be accomplished with a filter or a calculator employing, for example, a Fourier transform.
  • a control signal is generated in response to the speech component.
  • the control signal indicates one or more characteristics of the frequency distribution of the speech component corresponding to at least some of the frequency bands.
  • Weighting values are assigned to the frequency band signals in response to the values of the control signal.
  • the frequency band signals are altered in response to the weighting values to generate weighted frequency band signals.
  • the weighted frequency band signals are combined to generate a communication signal with enhanced quality.
  • the foregoing signal generation and manipulation of signals and values preferably is accomplished with a calculator.
  • Figures 1 A and IB are schematic block diagrams of known noise cancellation systems.
  • Figure 2 is a schematic block diagram of another form of a known noise cancellation system.
  • Figure 3 is a functional and schematic block diagram illustrating a preferred form of adaptive noise cancellation system made in accordance with the invention.
  • Figure 4 is a schematic block diagram illustrating one embodiment of the invention implemented by a digital signal processor.
  • Figure 5 is graph of relative noise ratio versus weight illustrating a preferred assignment of weight for va ⁇ ous ranges of values of relative noise ratios.
  • Figure 6 is a graph plotting power versus Hz illustrating a typical power spectral density of background noise recorded from a cellular telephone in a moving vehicle.
  • Figure 7 is a curve plotting Hz versus weight obtained from a preferred form of adaptive weighting function in accordance with the invention.
  • Figure 8 is a graph plotting Hz versus weight for a family of weighting curves calculated according to a preferred embodiment of the invention.
  • Figure 9 is a graph plotting Hz versus decibels of the broad spectral shape of a typical voiced speech segment.
  • Figure 10 is a graph plotting Hz versus decibels of the broad spectral shape of a typical unvoiced speech segment.
  • the preferred form of ANC system shown in Figure 3 is robust under adverse conditions often present in cellular telephony and packet voice networks. Such adverse conditions include signal dropouts and fast changing background noise conditions with wide dynamic ranges.
  • the Figure 3 embodiment focuses on attaining high perceptual quality in the processed speech signal under a wide variety of such channel impairments.
  • SPM Signal Activity Measure
  • the SPM is capable of detecting signal dropouts as well as new environments. Dropouts are temporary losses of the signal that occur commonly in cellular telephony and in voice over packet networks. New environment detection is the ability to detect the start of new calls as well as sudden changes in the background noise environment of an ongoing call.
  • the SPM can be beneficial to any noise reduction function, including the preferred embodiment of this invention.
  • Accurate noisy signal and noise power measures which are performed for each frequency band, improve the performance of the preferred embodiment.
  • the measurement for each band is optimized based on its frequency and the state information from the SPM.
  • the frequency dependence is due to the optimization of power measurement time constants based on the statistical distribution of power across the spectrum in typical speech and environmental background noise.
  • this spectrally based optimization of the power measures has taken into consideration the non-linear nature of the human auditory system.
  • the SPM state information provides additional information for the optimization of the time constants as well as ensu ⁇ ng stability and speed of the power measurements under adverse conditions. For instance, the indication of a new environment by the SPM allows the fast reaction of the power measures to the new environment
  • weighting functions are based on (1) the overall noise-to- signal ratio (NSR), (2) the relative noise ratio, and (3) a perceptual spectral weighting model.
  • the first function is based on the fact that over-suppression under heavier overall noise conditions provide better perceived quality.
  • the second function utilizes the noise cont ⁇ bution of a band relative to the overall noise to approp ⁇ ately weight the band, hence providing a fine structure to the spectral weighting.
  • the third weighting function is based on a model of the power-frequency relationship in typical environmental background noise. The power and frequency are approximately inversely related, from which the name of the model is de ⁇ ved.
  • the inverse spectral weighting model parameters can be adapted to match the actual environment of an ongoing call.
  • the weights are conveniently applied to the NSR values computed for each frequency band; although, such weighting could be applied to other parameters with approp ⁇ ate modifications just as well.
  • the weighting functions are independent, only some or all the functions can be jointly utilized
  • the preferred embodiment preserves the natural spectral shape of the speech signal which is important to perceived speech quality. This is attained by careful spectrally interdependent gam adjustment achieved through the attenuation factors.
  • An additional advantage of such spectrally interdependent gam adjustment is the va ⁇ ance reduction of the attenuation factors.
  • a preferred form of adaptive noise cancellation system 10 comp ⁇ ses an input voice channel 20 transmitting a communication signal comp ⁇ smg a plurality of frequency bands derived from speech and noise to an input terminal 22.
  • a speech signal component of the commumcation signal is due to speech and a noise signal component of the communication signal is due to noise.
  • a filter function 50 filters the communication signal into a plurality of frequency band signals on a signal path 51.
  • a DTMF tone detection function 60 and a speech presence measure function 70 also receive the communication signal on input channel 20.
  • the frequency band signals on path 51 are processed by a noisy signal power and noise power estimation function 80 to produce va ⁇ ous forms of power signals.
  • the power signals provide inputs to an perceptual spectral weighting function 90, a relative noise ratio based weighting function 100 and an overall noise to signal ratio based weighting function 110.
  • Functions 90, 100 and 110 also receive inputs from speech presence measure function 70 which is an improved voice activity detector
  • Functions 90, 100 and 110 generate preferred forms of weighting signals having weighting factors for each of the frequency bands generated by filter function 50.
  • the weighting signals provide inputs to a noise to signal ratio computation and weighting function 120 which multiplies the weighting factors from functions 90, 100 and 110 for each frequency band together and computes an NSR value for each frequency band signal generated by the filter function 50.
  • Some of the power signals calculated by function 80 also provide inputs to function 120 for calculating the NSR value.
  • a gain computation and interdependent gain adjustment function 130 calculates preferred forms of initial gain signals and preferred forms of modified gain signals with initial and modified gain values for each of the frequency bands and modifies the initial gain values for each frequency band by, for example, smoothing so as to reduce the variance of the gain.
  • the value of the modified gain signal for each frequency band generated by function 130 is multiplied by the value of every sample of the frequency band signal in a gain multiplication function 140 to generate preferred forms of weighted frequency band signals.
  • the weighted frequency band signals are summed in a combiner function 160 to generate a communication signal which is transmitted through an output terminal 172 to a channel 170 with enhanced quality.
  • a DTMF tone extension or regeneration function 150 also can place a DTMF tone on channel 170 through the operation of combiner function 160.
  • the function blocks shown in Figure 3 may be implemented by a variety of well known calculators, including one or more digital signal processors (DSP) including a program memory storing programs which are executed to perform the functions associated with the blocks (described later in more detail) and a data memory for storing the variables and other data described in connection with the blocks.
  • DSP digital signal processors
  • Figure 4 illustrates a calculator in the form of a digital signal processor 12 which communicates with a memory 14 over a bus 16.
  • Processor 12 performs each of the functions identified in connection with the blocks of Figure 3.
  • any of the function blocks may be implemented by dedicated hardware implemented by application specific integrated circuits (ASICs), including memory, which are well known in the art.
  • ASICs application specific integrated circuits
  • Figure 3 also illustrates an ANC 10 comprising a separate ASIC for each block capable of performing the function indicated by the block. Filtering
  • the noisy speech-containing input signal on channel 20 occupies a 4kHz bandwidth.
  • This communication signal may be spectrally decomposed by filter 50 using a filter bank or other means for dividing the communication signal into a plurality of frequency band signals.
  • the filter function could be implemented with block-processing methods, such as a Fast Fourier Transform (FFT).
  • FFT Fast Fourier Transform
  • the resulting frequency band signals typically represent a magnitude value (or its square) and a phase value.
  • the techniques disclosed in this specification typically are applied to the magnitude values of the frequency band signals.
  • Filter 50 decomposes the input signal into N frequency band signals representing N frequency bands on
  • the input to filter 50 will be denoted x(n) while the output of the k' h filter
  • the input, x( ⁇ ) , to filter 50 is high-pass filtered to remove DC components by
  • the gain (or attenuation) factor for the k' h frequency band is computed by function 130 once every T samples as
  • a suitable value for T is 10 when the sampling rate is 8kHz.
  • the gain factor will range between a small positive value, ⁇ , and 1 because the weighted NSR values are limited to he in the range [0,1- ⁇ ]. Setting the lower limit of the gain to ⁇ reduces the effects of "musical noise" (described in reference [2]) and permits limited background signal transparency. In the preferred embodiment, ⁇ is set to 0.05.
  • W k (n) is used for over-suppression and under-suppression pu ⁇ oses of the
  • the overall weighting factor is computed by function 120 as
  • u k (n) is the weight factor or value based on overall NSR as calculated by
  • w k (n) is the weight factor or value based on the relative noise ratio
  • function 140 by multiplying x k (n) by its corresponding gain factor, G k (n) , every
  • Combiner 160 sums the resulting attenuated signals, ⁇ ( ⁇ ) , to generate the enhanced output signal on channel
  • noisy signal power and noise power estimation function 80 include the calculation of power estimates and generating preferred forms of corresponding power band signals having power band values as identified in Table 1 below.
  • the power, P(n) at sample n, of a discrete-time signal u(n) is estimated approximately by either (a) lowpass filte ⁇ ng the full-wave rectified signal or (b) lowpass filte ⁇ ng an even power of the signal such as the square of the signal
  • a first order IIR filter can be used for the lowpass filter for both cases as follows:
  • the lowpass filte ⁇ ng of the full-wave rectified signal or an even power of a signal is an averaging process.
  • the power estimation (e.g., averaging) has an effective time window or time pe ⁇ od du ⁇ ng which the filter coefficients are large, whereas outside this window, the coefficients are close to zero
  • the coefficients of the lowpass filter determine the size of this window or time pe ⁇ od
  • the power estimation (e.g , averaging) over different effective window sizes or time periods can be achieved by using different filter coefficients.
  • the rate of averaging is said to be increased, it is meant that a shorter time period is used.
  • the power estimates react more quickly to the newer samples, and "forget" the effect of older samples more readily.
  • the rate of averaging is said to be reduced, it is meant that a longer time period is used.
  • the first order IIR filter has the following transfer function:
  • the coefficient, ⁇ is a decay constant.
  • the decay constant also represents how fast the old power value is forgotten and how quickly the power of the newer input samples is inco ⁇ orated.
  • larger values of ⁇ result in longer effective averaging windows
  • Such first order lowpass IIR filters may be used for estimation of the va ⁇ ous power measures listed in the Table 1 below-
  • Function 80 generates a signal for each of the foregoing Va ⁇ ables.
  • Each of the signals in Table 1 is calculated using the estimations desc ⁇ bed in this Power Estimation section.
  • the Speech Presence Measure which will be discussed later, utilizes short-term and long-term power measures in the first formant region. To perform the first formant power measurements, the input signal, x(n) , is lowpass
  • the filter has a cut-off frequency at 850 ⁇ z and has coefficients
  • time constants are examples of the parameters used to analyze a communication signal and enhance its quality.
  • NSR overall (n) at sample n is defined as
  • the NSR for the k' h frequency band may be computed as
  • Speech presence measure (SPM) 70 may utilize any known DTMF detection method if DTMF tone extension or regeneration functions 150 are to be performed.
  • SPM 70 primarily performs a measure of the likelihood that the signal activity is due to the presence of speech. This can be quantized to a discrete number of decision levels depending on the application. In the preferred embodiment, we use five levels.
  • the SPM performs its decision based on the DTMF flag and the LEVEL value.
  • the SPM also outputs two flags or signals, DROPOUT and NEWENV, which will be desc ⁇ bed in the following sections.
  • the novel multi-level decisions made by the SPM are achieved by using a speech likelihood related companson signal and multiple va ⁇ able thresholds.
  • a speech likelihood related compa ⁇ son signal we de ⁇ ve such a speech likelihood related compa ⁇ son signal by compa ⁇ ng the values of the first formant short-term noisy signal power estimate,
  • Phtsiin Phtsiin
  • Pht ur(n)- Multiple compa ⁇ sons are performed using expressions involving and Pi st Lii'i) as given in the preferred embodiment of equation (11) below.
  • the result of these compa ⁇ sons is used to update the speech likelihood related compa ⁇ son signal.
  • the speech likelihood related compa ⁇ son signal is a
  • the hangover counter, /. var can be assigned a va ⁇ able hangover pe ⁇ od that is
  • the inequalities of (11) determine whether P ⁇ it ,s ⁇ (n) exceeds P] it ,L ⁇ (n) by more
  • h M represents a preferred form of
  • comparison signal resulting from the comparisons defined in (11) and having a value representing differing degrees of likelihood that a portion of the input communication signal results from at least some speech.
  • the hangover period length can be considered as a measure that is directly proportional to the probability of speech presence. Since the SPM decision is required to reflect the likelihood that the signal activity is due to the presence of speech, and the SPM decision is based partly on the LEVEL value according to Table 1, we determine the value for LEVEL based on the hangover counter as tabulated below.
  • SPM 70 generates a preferred form of a speech likelihood signal having values corresponding to LEVELs 0-3.
  • LEVEL depends indirectly on the power measures and represents varying likelihood that the input communication signal results from at least some speech. Basing LEVEL on the hangover counter is advantageous because a certain amount of hyste ⁇ sis is provided. That is, once the count enters one of the ranges defined in the preceding table, the count is constrained to stay in the range for va ⁇ able pe ⁇ ods of time. This hyste ⁇ sis prevents the LEVEL value and hence the SPM decision from changing too often due to momentary changes in the signal power. If LEVEL were based solely on the power measures, the SPM decision would tend to flutter between adjacent levels when the power measures he near decision bounda ⁇ es.
  • a dropout is a situation where the input signal power has a defined attribute, such as suddenly dropping to a very low level or even zero for short durations of time
  • dropouts are often expe ⁇ enced especially in a cellular telephony environment. For example, dropouts can occur due to loss of speech frames in cellular telephony or due to the user moving from a noisy environment to a quiet environment suddenly. During dropouts, the ANC system operates differently as will be explained later.
  • Equation (8) shows the use of a DROPOUT signal in the long-term (noise) power measure. Du ⁇ ng dropouts, the adaptation of the long-term power for the SPM is stopped or slowed significantly. This prevents the long-term power measure from being reduced drastically during dropouts, which could potentially lead to incorrect speech presence measures later.
  • the SPM dropout detection utilizes the DROPOUT signal or flag and a
  • the counter is updated as follows every sample time.
  • the attribute of c dwpout determines at least in part the
  • comparison factor, ⁇ dropout is 0.2.
  • the background noise environment would not be known by ANC system 10.
  • the background noise environment can also change suddenly when the user moves from a noisy environment to a quieter environment e g moving from a busy street to an indoor environment with windows and doors closed. In both these cases, it would be advantageous to adapt the noise power measures quickly for a short pe ⁇ od of time.
  • the SPM outputs a signal or flag called NEWENV to the ANC system
  • the detection of a new environment at the beginning of a call will depend on the system under question. Usually, there is some form of indication that a new call has been initiated. For instance, when there is no call on a particular line in some networks, an idle code may be transmitted. In such systems, a new call can be detected by checking for the absence of idle codes. Thus, the method for mfer ⁇ ng that a new call has begun will depend on the particular system.
  • a pitch estimator is used to monitor whether voiced speech is present in the input signal. If voiced speech is present, the pitch pe ⁇ od (i.e., the inverse of pitch frequency) would be relatively steady over a pe ⁇ od of about 20ms. If only background noise is present, then the pitch pe ⁇ od would change in a random manner. If a cellular handset is moved from a quiet room to a noisy outdoor environment, the input signal would be suddenly much louder and may be incorrectly detected as speech. The pitch detector can be used to avoid such incorrect detection and to set the new environment signal so that the new noise environment can be quickly measured
  • any of the numerous known pitch pe ⁇ od estimation devices may be used, such as device 74 shown in Fig. 3.
  • the following method is used. Denoting K(n-T) as the pitch pe ⁇ od estimate from T samples ago, and K(n) as the current pitch pe ⁇ od estimate, if ⁇ K(n)- K(n-40) ⁇ >3, and ⁇ K(n-40)-K(n-80) ⁇ >3, and ⁇ K(n-80)-K(n-120) ⁇ >3, then the pitch pe ⁇ od is not steady and it is unlikely that the input signal contains voiced speech. If these conditions are true and yet the SPM says that LEVEL>1 which normally implies that significant speech is present, then it can be inferred that a sudden increase in the background noise has occurred.
  • the following table specifies a method of updating NEWENV and c nmem .
  • the NEWENV flag is set to 1 for a pe ⁇ od of time specified by
  • the NEWENV flag is set to 1 in response to
  • a suitable value for the c newmv max is 2000 which corresponds to 0.25 seconds
  • the multi-level SPM decision and the flags DROPOUT and NEWENV are generated on path 72 by SPM 70. With these signals, the ANC system is able to perform noise cancellation more effectively under adverse conditions. Furthermore, as previously desc ⁇ bed, the power measurement function has been significantly enhanced compared to p ⁇ or known systems. Additionally, the three independent weighting functions earned out by functions 90, 100 and 110 can
  • SPM 70 is indicating that there is a new environment due to either a new call or that it is a post-dropout environment. If there is no speech activity, i.e. the SPM indicates that there is silence, then it would be advantageous for the ANC system to measure the noise spectrum quickly. This quick reaction allows a shorter adaptation time for the ANC system to a new noise
  • the time constants ⁇ N k , ⁇ k , a N k and a s k are based on
  • the time constants are also based on the multi-level decisions of the SPM
  • SPM decisions there are four possible SPM decisions (i.e., Silence, Low Speech, Medium Speech, High Speech).
  • Silence When the SPM decision is Silence, it would be beneficial to speed up the tracking of the noise in all the bands.
  • the SPM decision When the SPM decision is Low Speech, the likelihood of speech is higher and the noise power measurements are slowed down accordingly. The likelihood of speech is considered too high in the remaining speech states and thus the noise power measurements are turned off in these states.
  • the time constants for the signal power measurements are modified so as to slow down the tracking when the likelihood of speech is low. This reduces the va ⁇ ance of the signal power measures du ⁇ ng low speech levels and silent pe ⁇ ods. This is especially beneficial du ⁇ ng silent pe ⁇ ods as it prevents short-duration noise spikes from causing the gam factors to ⁇ se.
  • over-suppression is achieved by weighting the NSR according
  • u k (n) 0.5 + NSR overall (n) (14)
  • a suitable update rate is once per 2T samples.
  • the relative noise ratio in a frequency band can be defined as
  • the goal is to assign a higher weight for a band when the ratio, R k (n) , for that
  • Function 80 ( Figure 3) generates preferred forms of band power signals corresponding to the terms on the right side of equation (15) and function 100 generates preferred forms of weighting signals with weighting values co ⁇ esponding to the term on the left side of equation (15).
  • Figure 6 shows the typical power spectral density of background noise recorded from a cellular telephone in a moving vehicle.
  • Typical environmental background noise has a power spectrum that corresponds to pink or brown noise.
  • Pink noise has power inversely proportional to the frequency.
  • Brown noise has power inversely proportional to the square of the frequency.
  • the weight, w f for a particular frequency, / can be modeled as a function
  • This model has three parameters ⁇ b, f 0 , c ⁇
  • the Figure 7 curve vanes monotomcally with decreasing values of weight from 0 Hz to about 3000 Hz, and also vanes monotomcally with increasing values of weight from about 3000 Hz to about 4000 Hz.
  • the ideal weights, w k may be obtained as a function of the measured noise
  • the ideal weights are equal to the noise power measures normalized by the largest noise power measure.
  • the normalized power of a noise component m a particular frequency band is defined as a ratio of the power of the noise component in that frequency band and a function of some or all of the powers of the noise components m the frequency band or outside the frequency band. Equations (15) and (18) are examples of such normalized power of a noise component. In case all the power values are zero, the ideal weight is set to unity. This ideal weight is actually an alternative definition of RNR.
  • the normalized power may be calculated according to (18) Accordingly, function 100 ( Figure 3) may generate a preferred form of weighting signals having weighting values approximating equation (18).
  • the approximate model in (17) attempts to mimic the ideal weights computed
  • the iterations may be performed every sample time or slower, if desired, for economy.
  • the weights are adapted efficiently using a simpler adaptation technique for economical reasons. We fix the value of the weighting
  • the weighting values so that they vary monotonically between two frequencies separated by a factor of 2 (e.g., the weighting values vary monotonically between 1000-2000 Hz and/or between 1500-3000 Hz).
  • the determination of c n is performed by comparing the total noise power in
  • lowpass and highpass filter could be used to filter x(n) followed by
  • the min and max functions rest ⁇ ct c n to he within [0.1,1.0].
  • a curve such as Figure 7, could be stored as a weighting signal or table in memory 14 and used as static weighting values for each of the frequency band signals generated by filter 50.
  • the curve could vary monotonically, as previously explained, or could vary according to the estimated
  • the power spectral density shown in Figure 6 could be thought of as defining the spectral shape of the noise component of the communication signal received on channel 20.
  • the value of c is altered according to the spectral shape in
  • weighting values determined according to the spectral shape of the noise component of the communication signal on channel 20 are denved in part from the likelihood that the communication signal is denved at least in part from speech.
  • the weighting values could be determined from the overall background noise power.
  • equation (17) is determined by the value of P BN (n)
  • the weighting values may vary in accordance with at least an approximation of one or more characte ⁇ stics (e.g., spectral shape of noise or overall background power) of the noise signal component of the communication signal on channel 20.
  • characte ⁇ stics e.g., spectral shape of noise or overall background power
  • the perceptual importance of different frequency bands change depending on charactenstics of the frequency distnbution of the speech component of the communication signal being processed. Determining perceptual importance from such characte ⁇ stics may be accomplished by a vanety of methods. For example, the characte ⁇ stics may be determined by the likelihood that a communication signal is denved from speech. As explained previously, this type of classification can be
  • the type of signal can be further classified by determining whether the speech is voiced or unvoiced.
  • Voiced speech results from vibration of vocal cords and is illustrated by utterance of a vowel sound.
  • Unvoiced speech does not require vibration of vocal cords and is illustrated by utterance of a consonant sound.
  • the actual implementation of the perceptual spectral weighting may be performed directly on the gam factors for the individual frequency bands.
  • Another alternative is to weight the power measures approp ⁇ ately. In our prefened method, the weighting is inco ⁇ orated into the NSR measures.
  • the PSW technique may be implemented independently or in any combination with the overall NSR based weighting and RNR based weighting methods.
  • the weights in the PSW technique are selected to vary between zero and one. Larger weights conespond to greater suppression
  • the basic idea of PSW is to adapt the weighting curve in response to changes in the characteristics of the frequency distribution of at least some components of the communication signal on channel 20.
  • the weighting curve may be changed as the speech spectrum changes when the speech signal transitions from one type of communication signal to another, e.g., from voiced to unvoiced and vice versa.
  • the weighting curve may be adapted to changes in the speech component of the communication signal.
  • the regions that are most critical to perceived quality are weighted less so that they are suppressed less. However, if these perceptually important regions contain a significant amount of noise, then their weights will be adapted closer to one.
  • v, b(k - k 0 ) 2 + c (30)
  • v k is the weight for frequency band k. In this method, we will vary only k 0
  • This weighting curve is generally U-shaped and has a minimum value of c at
  • the lowest weight frequency band, k 0 is adapted based on the likelihood of
  • k 0 is allowed to be in the
  • midband frequencies are weighted less in general.
  • lowest weight frequency band k 0 is placed closer to 4000Hz so that the mid to high
  • the lowest weight frequency band is varied with the speech likelihood related comparison signal as follows:
  • the minimum weight c could be fixed to a small value such as 0.25.
  • the regional NSR, NSR nal (k) is defined with respect to the minimum weight
  • the regional ⁇ SR is the ratio of the noise power to the noisy signal
  • the minimum weight c when the regional ⁇ SR is -15dB or lower, we set the minimum weight c to 0.25 (which is about 12dB). As the regional ⁇ SR approaches its maximum value of OdB, the minimum weight is increased towards unity. This can be achieved by adapting the minimum weight c at sample time n as
  • NSR overall (n) ⁇ 0.1778 -15dB + 0.08S , 0.1778 ⁇ NSR overall (n) ⁇ 1
  • the v ⁇ curves are plotted for a range of values of c and k 0 in Figures 11-13 to
  • processor 12 generates a control signal from
  • the likelihood signal can also be used as a measure of whether the speech is voiced or unvoiced. Determining whether the speech is voiced or unvoiced can be accomplished by means other than the likelihood signal. Such means are known to those skilled in the field of communications.
  • the characteristics of the frequency distribution of the speech component of the channel 20 signal needed for PSW also can be determined from the output of pitch estimator 74.
  • the pitch estimate is used as a control signal which indicates the characteristics of the frequency distribution of the speech component of the channel 20 signal needed for PSW.
  • the pitch estimate or to be more specific, the rate of change of the pitch, can be used to solve for k ⁇ , in equation (32). A slow rate of change would correspond to smaller JQ values, and vice versa.
  • the calculated weights for the different bands are based on an approximation of the broad spectral shape or envelope of the speech component of the communication signal on channel 20.
  • the calculated weighting curve has a generally inverse relationship to the broad spectral shape of the speech component of the channel 20 signal.
  • An example of such an inverse relationship is to calculate the weighting curve to be inversely proportional to the speech spectrum, such that when the broad spectral shape of the speech spectrum is multiplied by the weighting curve, the resulting broad spectral shape is approximately flat or constant at all frequencies in the frequency bands of interest. This is different from the standard spectral subtraction weighting which is based on the noise-to-signal ratio of individual bands.
  • PSW we are taking into consideration the entire speech signal (or a significant portion of it) to determine the weighting curve for all the frequency bands.
  • the weights are determined based only on the individual bands. Even in a spectral subtraction implementation such as in Figure IB, only the overall SNR or NSR is considered but not the broad spectral shape
  • the total power, P k (n) may be used to approximate the speech power
  • band power values together provide the broad spectral shape estimate or envelope estimate.
  • the number of band power values m the set will vary depending on the desired accuracy of the estimate. Smoothing of these band power values using moving average techniques is also beneficial to remove jaggedness in the envelope estimate.
  • the perceptual weighting curve may be determined to be inversely proportional to the broad spectral shape
  • the weight for the &* band, v k may be determined as
  • v ; (n) ⁇ I P (n) , where ⁇ is a predetermined value.
  • a set of speech power values such as a set of R (n) values, is used as a control signal
  • the va ⁇ ation of the power signals used for the estimate is reduced across the N frequency bands. For instance, the spectrum shape of the speech component of the channel 20 signal is made more nearly flat across the N frequency bands, and the vanation in the spectrum shape is reduced.
  • a paramet ⁇ c technique in our preferred implementation which also has the advantage that the weighting curve is always smooth across frequencies.
  • a paramet ⁇ c weighting curve i.e. the weighting curve is formed based on a few parameters that are adapted based on the spectral shape. The number of parameters is less than the number of weighting factors.
  • the parametnc weighting function in our economical implementation is given by the equation (30), which is a quadratic curve with three parameters.
  • a noise cancellation system will benefit from the implementation of only one or va ⁇ ous combinations of the functions.
  • the bandpass filters of the filter bank used to separate the speech signal into different frequency band components have little overlap Specifically, the magnitude frequency response of one filter does not significantly overlap the magnitude frequency response of any other filter in the filter bank This is also usually true for discrete Fou ⁇ er or fast Founer transform based implementations In such cases, we have discovered that improved noise cancellation can be achieved by interdependent gain adjustment Such adjustment is affected by smoothing of the input signal spectrum and reduction in vanance of gam factors across the frequency bands according to the techniques descnbed below.
  • the splitting of the speech signal into different frequency bands and applying independently determined ga factors on each band can sometimes destroy the natural spectral shape of the speech signal Smoothing the gain factors across the bands can help to preserve the natural spectral shape of the speech signal Furthermore, it also reduces the va ⁇ ance of the gain factors
  • the initial gam factors preferably are generated in the form of signals with initial gam values in function block 130 ( Figure 3) according to equation (1)
  • the initial gain factors or values are modified using a weighted moving average
  • the gain factors co ⁇ espondmg to the low and high values of k must be handled slightly differently to prevent edge effects.
  • the initial gain factors are modified by recalculating equation (1) in function 130 to a prefened form of modified gain signals having modified gain values or factors. Then the modified gain factors are used for gain multiplication by equation (3) in function block 140 ( Figure 3).
  • the M k are the moving average coefficients tabulated below for our preferred
  • coefficients selected from the following ranges of values are in the range of 10 to 50 times the value of the sum of the other coefficients.
  • the coefficient 0.95 is in the range of 10 to 50 times the value of the sum of the other coefficients shown in each line of the preceding table. More specifically, the coefficient 0.95 is in the range from .90 to .98.
  • the coefficient 0.05 is in the range .02 to .09.
  • the gain for frequency band k depends on NSR k (n) which in turn
  • G k ( ⁇ ) is computed as a function noise power and noisy signal power values from
  • G k (n) may be computed
  • Equations (1.1)-(1.4) All provide smoothing of the input signal spectrum and reduction in variance of the gain factors across the frequency bands. Each method has its own particular advantages and trade-offs.
  • the first method (1.1) is simply an alternative to smoothing the gains directly.
  • the method of (1.2) provides smoothing across the noise spectrum only while (1.3) provides smoothing across the noisy signal spectrum only.
  • Each method has its advantages where the average spectral shape of the corresponding signals are maintained. By performing the averaging in (1.2), sudden bursts of noise happening in a particular band for very short periods would not adversely affect the estimate of the noise spectrum. Similarly in method (1.3), the broad spectral shape of the speech spectrum which is generally smooth in nature will not become too jagged in the noisy signal power estimates due to, for instance, changing pitch of the speaker.
  • the method of (1.4) combines the advantages of both (1.2) and (1.3).

Landscapes

  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Noise Elimination (AREA)

Abstract

Selon l'invention, un système de communication servant à traiter un signal de communication provenant de la parole et du bruit améliore la qualité du signal de communication en fournissant un filtre (50) qui divise le signal de communication en plusieurs signaux de bandes de fréquence représentant le signal de communication dans plusieurs bandes de fréquence. Un calculateur génère un signal de vraisemblance présentant des valeurs qui représentent les vraisemblances selon lesquelles le signal de communication est issu de la parole (70). Le calculateur attribue des valeurs de pondération (120) aux signaux de bandes de fréquence en réponse aux valeurs du signal de vraisemblance. Le calculateur modifie également les signaux de bandes de fréquence en réponse aux valeurs de pondération afin de générer des signaux de bandes de fréquence pondérés, et combine (160) les signaux de bandes de fréquence pondérés pour générer un signal de communication de qualité améliorée (170).
PCT/US2001/006888 2000-03-28 2001-03-02 Ponderation spectrale perceptive de bandes de frequence pour une suppression adaptative du bruit WO2001073759A1 (fr)

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WO2008002874A3 (fr) * 2006-06-26 2008-03-20 Bose Corp Réglage des fuites d'un filtre adaptatif de réduction active du bruit
WO2009081191A1 (fr) * 2007-12-21 2009-07-02 Wolfson Microelectronics Plc Système d'annulation de bruit à gain dépendant du rapport signal-sur-bruit
WO2009081185A1 (fr) * 2007-12-21 2009-07-02 Wolfson Microelectronics Plc Système d'annulation de bruit comportant une commande de gain basée sur le niveau de bruit
WO2010048461A3 (fr) * 2008-10-23 2010-10-14 Temic Automotive Of North America, Inc. Masquage de bruit variable pendant des périodes de silence prononcé
US8204242B2 (en) 2008-02-29 2012-06-19 Bose Corporation Active noise reduction adaptive filter leakage adjusting
US8306240B2 (en) 2008-10-20 2012-11-06 Bose Corporation Active noise reduction adaptive filter adaptation rate adjusting
US8355512B2 (en) 2008-10-20 2013-01-15 Bose Corporation Active noise reduction adaptive filter leakage adjusting
US9173025B2 (en) 2012-02-08 2015-10-27 Dolby Laboratories Licensing Corporation Combined suppression of noise, echo, and out-of-location signals
US9584087B2 (en) 2012-03-23 2017-02-28 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement

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WO2007093726A3 (fr) * 2006-02-14 2007-10-18 France Telecom Dispositif de ponderation perceptuelle en codage/decodage audio
US8260620B2 (en) 2006-02-14 2012-09-04 France Telecom Device for perceptual weighting in audio encoding/decoding
WO2008002874A3 (fr) * 2006-06-26 2008-03-20 Bose Corp Réglage des fuites d'un filtre adaptatif de réduction active du bruit
EP2840569A1 (fr) * 2006-06-26 2015-02-25 Bose Corporation Réglage des fuites d'un filtre adaptatif de réduction active du bruit
US8194873B2 (en) 2006-06-26 2012-06-05 Davis Pan Active noise reduction adaptive filter leakage adjusting
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WO2009081191A1 (fr) * 2007-12-21 2009-07-02 Wolfson Microelectronics Plc Système d'annulation de bruit à gain dépendant du rapport signal-sur-bruit
WO2009081185A1 (fr) * 2007-12-21 2009-07-02 Wolfson Microelectronics Plc Système d'annulation de bruit comportant une commande de gain basée sur le niveau de bruit
US8204242B2 (en) 2008-02-29 2012-06-19 Bose Corporation Active noise reduction adaptive filter leakage adjusting
US8306240B2 (en) 2008-10-20 2012-11-06 Bose Corporation Active noise reduction adaptive filter adaptation rate adjusting
US8355512B2 (en) 2008-10-20 2013-01-15 Bose Corporation Active noise reduction adaptive filter leakage adjusting
WO2010048461A3 (fr) * 2008-10-23 2010-10-14 Temic Automotive Of North America, Inc. Masquage de bruit variable pendant des périodes de silence prononcé
CN102265334B (zh) * 2008-10-23 2013-03-06 大陆汽车系统公司 显著静默时段期间的可变噪声掩蔽
US8160271B2 (en) 2008-10-23 2012-04-17 Continental Automotive Systems, Inc. Variable noise masking during periods of substantial silence
US9173025B2 (en) 2012-02-08 2015-10-27 Dolby Laboratories Licensing Corporation Combined suppression of noise, echo, and out-of-location signals
US9584087B2 (en) 2012-03-23 2017-02-28 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement
US10311891B2 (en) 2012-03-23 2019-06-04 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement
US10902865B2 (en) 2012-03-23 2021-01-26 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement
US11308976B2 (en) 2012-03-23 2022-04-19 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement
US11694711B2 (en) 2012-03-23 2023-07-04 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement
US12112768B2 (en) 2012-03-23 2024-10-08 Dolby Laboratories Licensing Corporation Post-processing gains for signal enhancement

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EP1287521A4 (fr) 2005-11-16
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AU2001245418A1 (en) 2001-10-08

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