Classifications

• • 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
  • 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
  • G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
  • G10L21/0364—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
• • 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
• • 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
  • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
  • G10L25/90—Pitch determination of speech signals

Definitions

• the present invention relates to digital techniques for denoising speech signals. It relates more particularly to denoising by nonlinear spectral subtraction.
• This technique makes it possible to obtain an acceptable denoising for strongly voiced signals, but totally distorts the speech signal. Faced with a relatively coherent noise, such as that caused by the contact of car tires or the rattling of an engine, the noise can prove to be more easily predictable than the unvoiced speech signal. There is then a tendency to project the speech signal into a part of the vector space of the noise. The method ignores the speech signal, especially the unvoiced speech areas where the predictability is reduced. In addition, predicting the speech signal from a reduced set of parameters does not take into account all the intrinsic richness of the speech. We understand here the limits of techniques based solely on mathematical considerations while forgetting the particular character of speech. Finally, other techniques are based on consistency criteria.
• the coherence function is particularly well developed by JA Cadzow and 0. M. Solomon ("Lmear modelmg and the coherence function", IEEE Trans. On Acoustics, Speech and Signal Processing, Vol. ASSP-35, n ° 1, January 1987 , pages 19-28), and its application to denoising has been studied by R. Le Bouquin ( ⁇ . ⁇ Enhancement of noisy speech signais: application to moo radio communications ", Speech Communication, Vol. 18, pages 3-19) .
• This method is based on the fact that the speech signal has a significantly greater coherence than noise provided that several independent channels are used. The results seem to be quite encouraging. But unfortunately, this technique requires having multiple sources of sound, which is not always achieved.
• US Patent 5,228,088 describes a denoising system operating in the frequency domain, provided with a tone frequency detector. The result of this detection is used on the one hand to adjust the noise suppression coefficients, and on the other hand to locate a "voice band".
• the noise suppression coefficients are used by a spectral subtraction module to weight the noise estimate before subtracting it from the signal.
• the module which adjusts the suppression coefficients only uses the information according to which a tone frequency has been detected or not. However, the value taken by the tone frequency has no influence on the suppression coefficients used.
• the “voice band” determined using the detected tone frequency is subject to an overall signal enhancement. It can, as a variant, be used to determine a contrario a "noise band", band in which an overall attenuation is then applied.
• a main aim of the present invention is to propose a new denoising technique which takes into account the characteristics of speech production, thus allowing effective denoising without deteriorating the perception of speech.
• the invention thus proposes a method for denoising a digital speech signal processed by successive frames, in which:
• spectral subtraction is carried out comprising at least one step consisting in respectively subtracting, from each spectral component of the speech signal on the frame, a quantity depending on parameters including the increased estimate of the corresponding spectral component of the noise for said frame; and - a transformation to the time domain is applied to the result of the spectral subtraction to construct a denoised speech signal.
• a harmonic analysis of the speech signal is performed to estimate a tonal frequency of the speech signal on each frame where it exhibits speech activity.
• the parameters on which the quantities subtracted depend include the tone frequency thus estimated.
• the speech signal of the frame is conditioned by oversampling it at a oversampling frequency multiple of the estimated tonal frequency, and the components are calculated. spectral of the speech signal on the frame based on the conditioned signal to subtract said quantities from them. This arrangement makes it possible to favor the frequencies closest to the estimated tone frequency over the other frequencies. This avoids protecting harmonics relatively far from those of the tone frequency. The harmonic nature of the speech signal is therefore best preserved.
• the conditioned signal is distributed in blocks of N samples subjected to a transformation in the frequency domain, and the ratio between the oversampling frequency and the estimated tone frequency is chosen as a divisor of the number N .
• the previous technique can be further refined by estimating the tonal frequency of the speech signal on a frame as follows:
• the speech signal is interpolated in said time intervals, so that the conditioned signal resulting from this interpolation has a constant time interval between two consecutive breaks.
• This procedure artificially constructs a signal frame on which the speech signal breaks at constant intervals. We thus take into account possible variations in the tonal frequency over the duration of a frame.
• An additional improvement consists in that, after the processing of each frame, a number of samples equal to an integer multiple of times the ratio between the frequency of the noise-free speech signal provided by this processing is retained. sampling and estimated tone frequency. This avoids the problems of distortion caused by phase discontinuities between frames, which are generally not completely corrected by conventional overlap-add techniques.
• the fact of having conditioned the signal by the oversampling technique makes it possible to obtain a good measure of the degree of voicing of the speech signal on the frame, from a calculation of the entropy of the autocorrelation of the calculated spectral components. based on the signal conditions. The more the spectrum is disturbed, that is to say the more it is seen, the lower the values of the entropy.
• the conditioning of the speech signal accentuates the irregular aspect of the spectrum and therefore the variations of the entropy, so that this constitutes a measure of good sensitivity.
• the autocorrelations will usually be calculated based on the denoised signal to obtain the best performance. It would however be possible to calculate them on the basis of the conditioned signal before noise reduction.
• the spectral components of the noise signal obtained by subtracting said quantities from the spectral components of the speech signal, can be used to calculate a masking curve by applying a model of this auditory perception.
• the parameters on which the quantity subtracted from a spectral component of the speech signal on a frame depends then include a difference between the increased estimate oe the corresponding spectral component of the noise and the calculated masking curve.
• This subtracted quantity can in particular be limited to the fraction of the estimate increased by the corresponding spectral component of the noise which exceeds the masking curve. This procedure is based on the observation that it is sufficient to denoise the audible noise frequencies. Conversely, there is no point in eliminating noise which is masked by speech.
• each increased estimate of the noise included in the speech signal is obtained by combining a long-term estimate of said spectral component of noise, and a measure of the variability of said spectral component of noise around its estimate. long-term.
• FIG. 1 is a block diagram of a denoising system implementing the present invention
• FIGS. 2 and 3 are flowcharts of procedures used by a voice activity detector of the system of Figure 1;
• FIG. 4 is a diagram representing the states of a voice activity detection automaton
• FIG. 5 is a graph illustrating the variations of a degree of vocal activity
• FIG. 6 is a block diagram of a noise overestimation module of the system of Figure 1;
• FIG. 7 is a graph illustrating the calculation of a masking curve;
• - Figure 8 is a graph illustrating the operation of the masking curves in the system of Figure 1;
• FIG. 9 is a block diagram of another denoising system implementing the present invention.
• FIG. 10 is a graph illustrating a harmonic analysis method usable in a method according to the invention.
• FIG. 11 partially shows a variant of the block diagram of FIG. 9.
• the denoising system shown in FIG. 1 processes a digital speech signal s.
• the signal frame is transformed in the frequency domain by a module 11 applying a conventional fast Fourier transform (TFR) algorithm to calculate the module of the signal spectrum.
• TFR fast Fourier transform
• the frequency resolution available at the output of the fast Fourier transform is not used, but a lower resolution, determined by a number I of frequency bands covering the band [0 , F / 2] of the signal.
• a module 12 calculates the respective averages of the spectral components Si_l., 1 of the speech signal in bands, for example by a uniform weighting such that:
• This averaging decreases the fluctuations between the bands by averaging the noise contributions in these bands, which will decrease the variance of the noise estimator. In addition, this averaging allows a significant reduction in the complexity of the system.
• the averaged spectral components Sil, 1 • are addressed to a module 15 for detecting voice activity and to a module 16 for estimating noise. These two modules 15,
• the module 15 proceeds a priori to debruita ⁇ e the speech signal in the different bands i for the signal frame n.
• This a priori noise reduction is carried out according to a conventional process of non-linear spectral subtraction from noise estimates obtained during one or more previous frames.
• the module 15 calculates, with the resolution of the bands i, the frequency response Hp ⁇ n, i of the a priori denoising filter, according to the formula:
• s n, ⁇ ⁇ a n- ⁇ l, ⁇ - B n- ⁇ l, ⁇ Hp - b n- ⁇ 2, ⁇ or ⁇ l and ⁇ 2 are delays expressed in number of frames
• CA Il, - L is a noise overestimation coefficient, the determination of which will be explained below.
• Ep n are calculated according to:
• Ep fl max
• Steps 17 to 20 therefore essentially consist in subtracting from the signal spectrum an estimate, increased by the coefficient ⁇ n _ ⁇ - j _, of the noise spectrum estimated a priori.
• module 15 calculates, for each band i (O ⁇ i ⁇ I), a quantity ⁇ E, I_I, 1 representing the short-term variation of the energy of the noise-suppressed signal in band i, as well as 'a long-term value E n -, _ of the energy of the denoised signal in band i.
• the quantity ⁇ E 1, _1, 1 can be calculated by a simplified formula of
• step 26 the counter b is incremented by one unit in step 26.
• the long-term estimator ba is compared to the value of the smoothed energy E n / 1 . If ba ⁇ E n ⁇ , the estimator ba is taken equal to the smoothed value E n -, _ in step 28, and the counter o, is reset to zero. The quantity p, which is taken equal to the ratio ba / E n? 1 (step 36), is then equal to 1.
• step 27 shows that ba ⁇ E n -, _
• the counter b is compared with a limit value bmax in step 29. If b> bmax, the signal is considered to be too stationary to support vocal activity .
• step 34 the difference a ⁇ -bi ⁇ between the long-term estimator and the internal noise estimator is compared to a threshold ⁇ 2.
• the module 15 proceeds to the voice activity decisions in step 37.
• the module 15 first updates the state of the detection automaton according to the quantity p Q calculated for l of the signal band.
• the new state ⁇ of the automaton depends on the previous state ⁇ -, and of Pg, as shown in Figure 4.
• the module 15 also calculates the degrees of vocal activity ⁇ II, 1 • in each band i ⁇ l.
• This degree ⁇ critique-i is preferably a non-binary parameter, that is to say that the function ⁇ 11 is a function varying continuously between 0 and 1 according to the values taken by the quantity p.
• This function has for example the appearance shown in FIG. 5.
• Module 16 calculates the band noise estimates, which will be used in the denoising process, using the successive values of the components S_. and degrees of vocal activity ⁇ ⁇ ,.
• the module 16 updates the noise estimates per band according to the formulas:
• ⁇ n f ⁇ ⁇ B- ê n- + (1 - ⁇ S ) - 5 n, i (5 > / 14739
• the long-term noise estimates B n ⁇ are overestimated, by a modu_e 45 (FIG. 1), before proceeding to denoising by nonlinear spectral subtraction.
• Module 45 calculates the overestimation coefficient ⁇ , previously
• I basically has OA li f -.L. B i- ⁇ , f -L ..
• the organization of the overestimation module 45 is shown in FIG. 6.
• the increased estimate B n . is obtained by combining the long-term estimate JE. and an
• measure AB TM has the variability of the noise component in band i around its long-term estimate.
• this combination is essentially a simple sum made by an adder 46. It could also be a weighted sum.
• band i It is a function of the deviations S n-k, i - B n-k, i calculated for a number K of frames of silence (n-k ⁇ n). In the example shown, this function is simply the maximum (block 50). For each frame n, the degree of voice activity ⁇ I-.I, 1 • is compared to a threshold (block 51)
• FIFO FIFO
• ⁇ II, 1 • does not exceed the threshold (which can be equal to 0 if the function g () has the form of figure 5)
• FIFO 54 is not supplied, while it is in the opposite case.
• the maximum value contained in FIFO 54 is then provided as a measure of variability .
• the measure of variability AB I "l f J, .- can, as a variant, be obtained as a function of the values S grip (and not S n •) and
• the major estimator B n * f -_I provides excellent robustness to the musical noises of the denoising process.
• a first phase of the spectral subtraction is carried out by the module 55 shown in FIG. 1. This pnase provides, with the resolution of the bands i
• the coefficient ⁇ " ⁇ represents, like the coefficient ⁇ p of formula (3), a floor conventionally used to avoid negative or too low values of the denoised signal.
• this function decreasing according to the estimated value of the signal-to-noise ratio.
• This function is then equal to n 2 for the lowest values of the signal-
• this function decreases towards zero for the highest values of the signal / noise ratio. protect the most energetic areas of the spectrum, where the signal - speech is the most significant, the quantity subtracted from the signal then tending towards zero.
• This strategy can be refined by applying it selectively to the harmonics of the pitch frequency of the speech signal when it has vocal activity.
• a second denoising phase is carried out by a module 56 for protecting harmonics.
• This module calculates, with the resolution of the Fourier transform, the frequency response H n f of a second filter of
• the module 57 can apply any known method of analysis of the speech signal of the frame for determine the period T, expressed as an integer or fractional number of samples, for example a linear prediction method.
• the protection provided by the module 56 may consist in carrying out, for each frequency f belonging to a band i:
• the floor coefficients ⁇ 2 2 express the fact that certain harmonics of the tonal frequency f can be masked by noise, so that it is not useful to protect them.
• This protection strategy is preferably ⁇ applied for each of the frequencies closest to the harmonic frequencies of f, that is to say for any arbitrary integer.
• condition (9) the difference between the ⁇ -th harmonic of the real tone frequency is its estimate f n (condition (9)) can go up to ⁇ ⁇ x ⁇ f / 2.
• this difference can be greater than the spectral half-resolution ⁇ f / 2 of .5 the Fourier transform.
• condition (9) one can protect each of the frequencies of the interval ⁇ xf p - ⁇ x ⁇ jf / 2, ⁇ xf ' + ⁇ x ⁇ i / 2, i.e. replace condition (9) above with:
• the corrected frequency response H n ⁇ can be equal to 1 as indicated above, which corresponds to the subtraction of a zero quantity within the framework of spectral subtraction, 0 i.e. tell a full protection of the frequency in question. More generally, this frequency response corrected H ⁇ t.A f could be taken equal to a value
• the spectral components 5 I n l f J f . of a noise signal are calculated by a multiplier 58:
• This signal S nf is supplied to a module 60 which calculates, for each frame n, a masking curve by applying a psychoacoustic model of auditory perception by the human ear.
• the masking phenomenon is a known principle of the functioning of the human ear. When two frequencies are heard simultaneously, one of them may no longer be heard. We then say that it is masked.
• the masking curve is seen as the convolution of the spectral spreading function of the basilar membrane in the bark domain with the excitatory signal, constituted in the present application by the signal S n ⁇ .
• the spectral spreading function can be modeled as shown in Figure 7.
• the masking threshold réelle q is obtained by the module
• R depends on the more or less voiced character of the signal.
• R depends on the more or less voiced character of the signal.
• the parameter ⁇ can be of the known form:
• the denoising system also includes a module
• Module 62 decides the level of denoising which must really be achieved.
• the new response H nf for a frequency f belonging to the band i defined by the module 12 and to the bark band q, thus depends on the relative difference between the increased estimate B n of the corresponding spectral component of the noise and the masking curve v_ ⁇ _. as follows :
• H n 3 f is substantially equal to the minimum between on the one hand the quantity subtracted from this spectral component in the process of spectral subtraction having the frequency response H n f, and on the other hand the fraction of
• FIG. 8 illustrates the principle of the correction applied by the module 62. It schematically shows an example of a masking curve M, n., Q calculated on the basis
• the quantity finally subtracted from the components Sn, r will be that represented by the hatched areas, that is to say limited to the fraction of the increased estimate B. spectral components of the noise that exceeds the masking curve. This subtraction is done by multiplying the
• a module 65 reconstructs the noise signal in the time domain, by operating the inverse fast Fourier transform (TFRI) of the
• FIG. 9 shows a preferred embodiment of a denoising system implementing the invention.
• This system comprises a certain number of elements similar to corresponding elements of the system of FIG. 1, for which the same reference numbers have been used. So, modules 10, 11,
• Fast Fourier 11 is a limitation of the system of FIG. 1.
• the frequency subject to protection by the module 56 is not necessarily the precise tone frequency f, but the frequency closest to it. in the discrete spectrum. In some cases, it is then possible to protect harmonics relatively far from that of the tone frequency.
• the system of FIG. 9 overcomes this drawback thanks to an appropriate conditioning of the speech signal.
• the sampling frequency of the signal is modified so that the period 1 / f covers exactly an integer number of sample times of the conditioned signal.
• Many methods of harmonic analysis that can be implemented by the module 57 are capable of providing a fractional value of the delay T, expressed as a number of samples at the initial sampling frequency F.
• a new sampling frequency f is then chosen so that it is equal to an integer multiple of the estimated tone frequency, ie F ti, with whole p. In order not to lose signal samples, f should be greater than F.
• F One can in particular impose that it is between F ⁇ and 2F (1 ⁇ K ⁇ 2), to facilitate the implementation of the conditioning.
• This size N is usually a power of 2 for the implementation of the TFR. It is 256 in the example considered.
• This choice is made by a module 70 according to the value of the delay
• the module 70 provides the ratio K between the sampling frequencies to three frequency change modules 71, 72, 73.
• the module 71 is used to transform the values S J n beau, i '
• the module 72 proceeds to oversampling the frame of N samples provided by the windowing module 10.
• Oversampling in a rational factor K K1 / K2 consists in first carrying out an oversampling in the integer factor K1, then a sub-sampling in the integer factor K2.
• the conditioned signal frame supplied by the module 72 includes KN samples at the frequency f. These samples are sent to a module 75 which calculates their Fourier transform.
• the two blocks therefore have an overlap of (2-K) xl00%.
• S_ Il - For each of the two blocks, we obtain a set of Fourier components S_ Il -.
• These components S_II f J are supplied to the multiplier 58, which multiplies them by the spectral response H n 2 f to deliver the spectral components S n ? ⁇ of the first denoised signal.
• the module 60 which calculates the masking curves in the manner previously indicated.
• the autocorrelations A (k) are calculated by a module 76, for example according to the formula: N / 2-1
• a module 77 then calculates the normalized entropy
• the standardized entropy H constitutes a measurement of voicing very robust to noise and variations in the tonal frequency.
• the correction module 62 operates in the same way as that of the system in FIG. 1, taking into account the overestimated noise B n ⁇ resized by the frequency change module 71. It provides the frequency response H n ⁇ of the final noise reduction filter, which is multiplied by the spectral components Sn n , r of the signal conditioned by the multiplier
• a module 80 combines, for each frame, the two signal blocks resulting from the processing of the two overlapping blocks delivered by TFR 75. This combination can consist of a sum with Hamming weighting of the samples, to form a denoised conditioned signal frame of K ⁇ samples.
• the management module 82 manages the windows formed by the module 10 and saved by the module 66, so that a number M of samples is saved equal to an integer multiple of. This avoids the problems of phase discontinuity between the frames.
• the management module 82 controls the windowing module 10 so that the overlap between the current frame and the next one corresponds to NM. This recovery of NM samples will be required in the recovery sum carried out by the module 66 during the processing of the next frame. From the value of T provided by the harmonic analysis module 57, the module 82 calculates the number of samples to be saved
• M T xE [N / (2T)], E [] designating the whole part, and correspondingly controls the modules 10 and 66.
• the tonal frequency is estimated on an average basis on the frame.
• the tonal frequency may vary somewhat over this period. It is possible to take these variations into account in the context of the present invention, by conditioning the signal so as to artificially obtain a constant tone frequency in the frame.
• the harmonic analysis module 57 provide the time intervals between the consecutive ruptures of the speech signal att ⁇ buables at closings of the glottis of the intervening speaker for the duration of the frame.
• Methods usable for detecting such micro-ruptures are well known in the field of harmonic analysis of speech signals.
• the principle of these methods is to perform a statistical test between two models, one in the short term and the other in the long term. Both models are adaptive linear prediction models.
• the value of this statistical test wm. is the cumulative sum of the posterior likelihood ratio of two distributions, corrected by the Kullback divergence. For a distribution of residuals having a Gaussian statistic, this value w.m is given by:
• FIG. 10 thus shows a possible example of evolution of the value w, showing the breaks R of the speech signal.
• FIG. 11 shows the means used to calculate the conditioning of the signal in the latter case.
• the harmonic analysis module 57 is produced so as to implement the above analysis method, and to provide the intervals t relative to the signal frame produced by the module 10.
• These reports oversampling K r are supplied to the frequency change modules 72 and 73, so that the interpolations are carried out with the sampling ratio K over the corresponding time interval t.
• the largest T of the time intervals t supplied by the module 57 for a frame is selected by the module 70 (block 91 in FIG. 11) to obtain a torque p, ⁇ as indicated in table I.
• the module 56 for protecting the harmonics of the tone frequency operates in the same manner as above, using for condition (9) the spectral resolution ⁇ f provided by the block 91 and the tone frequency f -f / p defined according to the value of the integer delay p supplied P * - by block 91.
• This embodiment of the invention also involves an adaptation of the window management module 82.
• the number M of samples of the denoised signal to be saved on the current frame here corresponds to an integer number of consecutive time intervals t between two glottal breaks (see FIG. 10). This arrangement avoids the problems of phase discontinuity between frames, while taking into account the possible variations of the time intervals t on a frame.

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