EP2078301A1

EP2078301A1 - Noise and distortion reduction in a forward-type structure

Info

Publication number: EP2078301A1
Application number: EP07823855A
Authority: EP
Inventors: André Gilloire; Mohamed Djendi; Pascal Scalart
Original assignee: France Telecom SA
Current assignee: Orange SA
Priority date: 2006-09-28
Filing date: 2007-09-26
Publication date: 2009-07-15
Also published as: WO2008037925A1

Abstract

The invention relates to a noise reduction structure of the forward type comprising at least two adaptive filtration channels with noise reduction (w <SUB>12</SUB>

Description

Noise and distortion reduction in a forward type structure

The present invention relates to a signal processing, in particular a speech signal in telephony.

The boom in telecommunications has enabled the general public to benefit from mobile communication tools. It has now become possible and common to telephone from anywhere (of course in the extent of network coverage areas) in environments such as a street, a train station or a vehicle. Nevertheless, such places do not enjoy the calm of a home and the comfort of communication that still offers fixed telephony. The annoyance due to the disturbance described as "noise" is generally a source of discomfort and is further accentuated by the dematerialisation of sound recording (the so-called "hands-free" system) which still encourages the emergence of noise due to increasing the distance between the mouth of the speaker and the microphone.

Under these conditions, there is the need for a treatment capable of reducing the annoyance caused by the additional noises to a speech signal and in particular extracting useful information from a noisy observation signal. In addition to this "denoising" application, such processing would find an advantageous implementation in voice recognition, the performance of which is strongly altered when the user is immersed in a noisy environment.

Successive approaches in the state of the art to remedy such a problem are described below. These approaches are based on the processing of signals from at least two sensors.

The principle of adaptive noise cancellation (ANC), typically using a stochastic gradient (Least Mean Squares) algorithm, is illustrated schematically in Figure 1. An input signal x constitutes a useful component and to which is naturally added a noise component, and a noise reference b ₂ correlated to the noise component added to the useful signal x, are propagated up to a treatment stage (right part of Figure 1). The treatment can be described as follows.

We consider a noisy observation y _\ (n) of the signal x (n) including the useful component, with yι {n) = x {n) + b _\ {n), where b _\ (n) is a decorrelated noise contribution the useful signal x (n). It is assumed that it has, at a second sensor, a noise reference b ₂ ( _")> correlated with b _\ (n) but uncorrelated with x (n). The relation between b _\ {n) and ^ W ^is assumed to be linear, with: b _\ {n) = H * Z ^ W ^where I ^e sign "*" represents the convolution product and H an unknown filter, to be determined.

The principle of adaptive cancellation ANC noise (for "Adaptive Noise Canceller") is the filtering of the reference b ₂ ( ") adequately in order to obtain the best estimate of b _j (n) (as defined in minimum mean squared error), which brings a reduction in output noise.

Taking into account the linear relation between the noises in the two observations, the output signal S can be written in the form:

S (n) = x (n) + (H - W) * b ₂ (n) (1)

The solution of an ideal denoising given by s (n) = x (n) amounts to finding an ideal cancellation filter W _opt such that W _opt = H. One way of determining the filter W _opt without knowledge either of the signal x (n) or of the filter H consists in considering the solution resulting from the minimization of the energy of the estimated signal s (n). Indeed, considering the decorrelation between bi (n) and x (n), we have from relation (1): E [S ² GI)] = E [X ² GI]] + E [{( H -W) * b ₂ (n)) ² ] (2) The minimum value of E [s (n) \ is then reached when the second right term of equation (2) is minimal (ideally for W _opt = H). The solution of this problem corresponds to the Wiener filter defined by:

W _t {z) = ^{i 2} _M (3) yb ₂ b ₂ UJ where the function γ _VlV2 denotes the spectral intercorrelation density between the signals V ₁ and V ₂ defined by:

where V \ (z) and V2 \ ^z / respectively represent the Z transforms of vj («) and

^V 2 \ - n) -

In the absence of information on the second-order statistics of observations, an alternative to the solution of equation (3) is to perform an adaptive estimation of H. We then choose a parametric model of W in the form, for example, of a finite impulse response (FIR) filter whose coefficients are estimated

((w (k)) _{k> = Q} ) according to an adaptive algorithm. Thus, the use of an LMS rule leads to the stochastic scheme: w (n + 1, k) = w (n, k) + μs {np ₂ (n - Jc), for k ≥ 0 (5) where w (n, k) denotes the value of the coefficient w (k) of order k of W (z) at the nth iteration.

The LMS algorithm in the sense of equation (5) only converges to an approximation of the Wiener solution, because: • an approximation of a Wiener filter (a priori with infinite memory and not necessarily causal) by a causal structure with finite memory, and The adaptive algorithms in the sense of equation (5) introduce misalignments due to the use of a non-asymptotically zero adaptation step. It is of course possible to choose an adaptation step itself adaptive to mitigate the misalignments, but without eliminating them.

Thus, the sound taken by a multi-sensor system (called "multidirectional") a priori allows better noise reduction performance than a traditional sound pickup from a single sensor.

However, in practice, the reference noise b ₂ is often mixed with a component from the wanted signal. This is particularly the case when the sensors are spatially close. The model of the mixture is now based on two filters h ^ iz) and h, 2 \ {z) which represent the physical (for example acoustic) coupling paths between the source signals and the sensors, as illustrated in Figure 2, representing a mixing model of the input signals X ₁ (n) and x ₂ (n), coming for example from two respective microphones of a sound acquisition module.

Thus, in the case of compact terminals, in which the sensors are close to each other, the signals picked up by the microphones contain mixtures of speech and noise. Second-order source separation techniques (without the use of higher-order statistics) make it possible under certain conditions to extract speech from noise with a minimum of damage.

In the state of the art, two conceptually simple structures have been proposed to achieve noise reduction by source separation. They are classically referred to respectively: - "backward structure", described in particular in:

"Improved adaptive noise cancellation in the presence of signal leakage on the noise reference channel", MJ. Al-Kindi and J. Dunlop, Signal Processing, vol.17, no.3, p.241-250, July 1989; and - "structure forward", described in particular in:

"Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness", S. Van Gerven and D.Van Compernolle, IEEE Trans. Signal Processing, Vol. 43 No.7, p.1602-1612, July 1995.

The forward structure can be considered as an extension of the basic structure of adaptive echo cancellation ANC. It solves the problem of the presence of the useful signal in the reference channel by symmetrizing the noise cancellation model. This source separation structure, however, has the disadvantage of distorting the output signals, even if it has theoretically been shown that the correction of the distortions would be possible thanks to a processing of the output signals by post-filtering, in Van Gerven and al (equation (12) page 1604). It seems that no satisfactory solution in practice has been proposed to implement this post-filtering.

Thus, these two source separation structures, backward and forward, have been proposed in the state of the art in order to separate the components of the mixtures resulting from the model illustrated in FIG. 2. These two structures are respectively represented by the figures. 3 and 4 and theoretically solve the problem due to the presence of the useful signal in the reference channel, by symmetrizing the noise cancellation model.

Treatments of the type shown in FIGS. 3 and 4 have initially been proposed for denoising in the presence of two speech signals, but their use for other types of signals is conceivable provided, in particular, that the mixing model of FIG. 2. These treatments can also be generalized to any number of input and output components in equal numbers.

FIG. 3 illustrates a symmetrical structure of the "backward" type, of denoising in the sense of the reference "Al-Kindi and Dunlop", mentioned above. Figure 4 illustrates a symmetrical structure "forward" denoising within the meaning of the reference "Van Gerven and Van Compernolle", supra. In general, it will be remembered that the structures illustrated in FIGS. 3 and 4 reduce the denoising of the observations to a problem of identification of an inverse system. The forward source separation structure, in particular, has a convergence advantage provided towards the solution but which requires the use of a post-filter causing problems in extracting the output signals. This structure is detailed below.

According to the model of FIG. 5, which generalizes the model of FIG. 2, the convolutional mixing output signals p _j (n) and p ₂ ("), which will be used as inputs of the forward source separation structure of Figure 4, are given by:

_Pι (n) = h _n * s (n) + h ₂ fb {n) + n _x (n) (6) and P ₂ (n) = h ₂₂ * b {n) + h ₁₂ * s (n) + n ₂ (n) (7) where: h _u and Ii22 (not shown) represent the impulse responses of each channel separately, hγi and / * ₂ i represent the effects of the mutual coupling between the two channels,

- s (ή) and b (n) are, respectively, two spatially punctual sources of useful signal (for example speech) and noise, nγ and "2 represent additive background noise, uncorrelated signals s (ή) and b (n),

- the symbol "*" representing, of course, the convolution operation.

In FIG. 2, as in FIG. 5, which illustrates the signal mixing model, the filters h _xx and / 1 ₂₂ ^are assumed to be "identity" filters, which does not affect the practical use of the model since User speaker of a multi-sensor terminal is expected to stay close to the microphones. This hypothesis also reflects the fact that we generally do not have information a priori on the location of source of noise (supposedly point). Note that / ι ₁₂ and / ι ₂₁ are generally non-stationary. In order to separate the components of the mixtures resulting from the model described with reference to FIG. 5, the forward separation structure of FIG. 6 can be used. FIG. 6, showing the forward separation structure of the mixtures, complete (with post-distortion reducing filters), then comprises two adaptation loops of the two filters w ₁₂ (z) and w ₂₁ (z), as well as the two PF1 and PF2 post-filters applied to the respective outputs Sι (n) and s ₂ (n). In theory, the two post-filters allow the perfect extraction (without distortion) of the original signals from the signals of the mixture.

It has been observed that the minimization of the correlation between the two outputs of this structure of FIG. 6 exactly amounts to minimizing the mean squared error of each output. The two outputs u _\ in) and u ₂ in) of the separation structure are calculated as follows: u _\ (n) = pi in) - p ₂ in) * W ₂₁ in) and U ₂ in) = p ₂ in ) - pγ in) * w _{\ 2} in)

By replacing the expressions of p _\ (n) and p ₂ (n) given by equations (6) and (7) in the expressions of and u ₂ in) and with h _n and h ₂₂ equal to the identity, we find:

M ₁ (n) = bin) * [h ₂ ι in) - w _{2 \} in) \ + sin) * [δin) - hγ ₂ in) * w _{2 \} in) \ + n _\ in) - n ₂ in ) * w _{2 \} in) u ₂ in) = sin) * [hι ₂ in) - wι ₂ in) \ + bin) * \ δin) -h ₂ ιin) * wι ₂ in) \ + n ₂ in) - nγin) * Wγ ₂ in)

The optimal theoretical solutions for the two expressions of u _\ in) and u ₂ in) are obtained respectively when w ₂ χ = h _2i and W ₁₂ = h _i2 . In this case, and in the absence of diffuse noise components (j in) and n ₂ in) on two sensors, the output signals are given by:

sin) = u _\ in) * [δin) -

The expressions of the two theoretical post-filters involved in the calculation of the output signals of the forward source separation structure are thus given by the following equations: for the post-filter PF1: [δ (n) -h ₁₂ (n) * (8)

- and for the PF2 post-filter: [δ (n) - h _2l (n) * w _l2 (n) \ (9)

Thus, when the two adaptive filters w _l2 (n) and w _2l (n) converge to their theoretical solution w _{2 \} = h ₂ χ and w _{\ 2} = h _{\ 2} , the two post-filters PF1 and PF2 tend to

to the same ideal solution: | δ (ra) - h _i2 (n) *

However, in practice, the direct obtaining of these two post-filters in the form of equations (8) and (9) is difficult, because in general we do not know a priori the filter hχ ₂ in equation (8) and the filter / i ₂ i in equation (9). By replacing / i ₂ i P ^{ar W} _{2 \} ^and h _{\ 2} by w _{\ 2} , one can obtain estimates of these two post-filters according to the theoretical expressions of equations (8) and (9). This scheme is nevertheless difficult to apply in practice because the estimates w _{2 \} and especially w _{\ 2} are disturbed by the technique of estimating these filters by adaptive filtering. To be able to directly calculate these two post-filters from their expressions given by equations (8) and (9), their inverse must have a minimal-phase characteristic, which is generally not the case. in practice. Therefore, this structure, as described with reference to FIG. 6, gives unsatisfactory results without the use of other methods and means for calculating the post-filters.

The present invention improves the situation.

Its purpose is the determination of satisfactory post-filters so as to minimize the distortion of the output signals, in particular on an output speech signal.

It proposes for this purpose a device for reducing noise in at least one signal, comprising: a structure of the forward type with at least two adaptive filtering channels with noise reduction on two input signals, for delivering two filtered and noise-reduced signals, and at least one post-filter at the output of a channel chosen from among both channels, to reduce distortion on the filtered signal of said selected channel.

Within the meaning of the invention, this post-filter comprises an adaptation means according to a comparison involving the input signal of said chosen channel.

This adaptation means can be constituted by an open loop path or an adaptation feedback.

In a first embodiment, the post-filter includes adaptive adaptive filtering feedback, based on a recursive comparison based on the difference between the output signal and the input signal of said selected channel.

In a second embodiment, the post-filter comprises an open loop frequency equalizing filter matching means, according to a comparison based on a ratio of power spectral densities, respectively between the filtered signal and the input signal. said chosen channel, brought back to the frequency domain.

In a third embodiment, the post-filter comprises adaptive adaptive filter adaptive feedback, according to a recursive comparison based on the difference between the output signal and the input signal, brought back into the frequency domain.

Other characteristics and advantages of the invention will appear on examining the detailed description below, and the appended drawings in which, in addition to FIGS. 1 to 6 described above:

FIG. 7 illustrates a noise reduction device comprising a two-stage forward structure with post-filtering implementing a feedback of adataption in the sense of the invention, by temporal adaptive filtering according to the first embodiment mentioned above,

FIG. 8 illustrates a noise reduction device comprising a two-stage forward structure with post-filtering implementing a means of adataption in the sense of the invention, by open loop frequency equalizer filtering according to the second embodiment. supra,

FIG. 9 illustrates a noise reduction device comprising a two-stage forward structure with post-filtering implementing adataption feedback within the meaning of the invention, by adaptive frequency matching filtering according to the third embodiment mentioned above,

FIG. 10 schematically illustrates telecommunication equipment, such as a telephony terminal, comprising a sound acquisition module including two microphones connected to a noise reduction device in the sense of the invention, and FIG. 11 illustrates schematically the steps of a method in the sense of the invention, for the implementation of a treatment according to one of the second or third embodiments mentioned above.

With reference to FIGS. 7 to 9, in the application of the invention for denoising purposes, essentially only one output of a noise-free speech signal which corresponds to the signal path u \ {n) is considered. of Figure 6 presented previously. This approach in the sense of the invention typically differs from the approaches of the prior art given above, in particular:

"Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness", S. Van Gerven and D.Van Compernolle, IEEE

Trans. Signal Processing, Vol. 43 No.7, p.1602-1612, July 1995.

In a general manner, in FIGS. 7 to 9 in the sense of the invention, given by way of example, the noise reduction structure, of the forward structure type, comprising: a first input for receiving a first original signal pi (n) , and at least one second input for receiving a second original signal p ₂ (n). The first and second signals have two respective substantially correlated noise versions. The structure further comprises:

a first filter Wn (Z) (optional), of adaptive noise reduction filter type, applied to the first signal,

a second filter W ₂₁ (z), of adaptive noise reduction filter type, applied to the second signal,

a first subtracter Ss ₁ between the first signal and the second filtered signal, for delivering a third signal ui (n), the third signal being of reduced noise and corresponding to the first signal to which the second filtered signal is subtracted,

a second subtracter ss ₂ (optional) between the second signal and the first filtered signal to deliver a fourth signal u ₂ (n), the fourth signal being of reduced useful signal component and corresponding to the second signal from which the first signal is subtracted filtered. The third signal feeds the second filter for adaptive feedback and the fourth signal feeds the first filter for adaptive feedback. The forward structure further comprises, in the example shown in FIGS. 7 to 9:

a first post-filter, a distortion reducer, applied to the third signal ui (n), for delivering a fifth signal S ₁ (n), in a first output of the structure, and a second post-filter w _p2 (z) ) (optional), a distortion reducer applied to the fourth signal u ₂ (n), to (optionally) deliver a sixth signal s ₂ (n) to a second output of the structure.

According to the invention, the aforementioned first post-filter, at least, comprises an adaptation means according to a comparison involving the first signal pi (n) and: the fifth signal si (n) in the first signal (FIG. 7) and third (FIG. 9) embodiments, or the third signal U ₁ (n) in the second embodiment (FIG. 8), as will be seen below. Two possible approaches, within the meaning of the invention, are presented below for the implementation of the post-filter of the signal path p _γ (").

The first possible approach, but with some disadvantages explained below, is based on a direct calculation of gain in the time domain, corresponding to a convergent theoretical post-filter. In the second approach, a frequency domain calculation is preferred.

We describe here the first approach, according to a forward structure with calculation of the post-filter by temporal adaptive filtering. A form of the source separation structure, with post-filtering, is given in FIG.

The peculiarity of this structure, modified with respect to the original structure of FIG. 6, is such that the post-filter given by equations (8) and (9) and presented in the basic structure is estimated by means of the filter adaptive w _p which is adapted by the feedback of the difference between its output and that of the mixture, or "filtering error". It is therefore necessary to estimate by adaptive identification the post-filter whose theoretical expression is given by equation (8) by minimizing the energy of the difference between the two signals pι (ή) and s \ (ή ) (respectively corresponding to the first original input signal and the fifth output signal of the forward structure). In practice, the average squared error is preferably minimized. Note that the filter w _p can be a finite impulse response (FIR) filter and can be updated from a formula of the type: w _pi in) = w _pi \ n-lj + μe (n) uι ( not) ,

where e (n) is the filtering error given by: e (n) = / J ₁ Oi) - S ₁ Oi), and μ a multiplicative coefficient. It is noted that the filter w _ft acts as a time equalizer, at each iteration n, of the result of the processing of the stage which precedes it, that is to say of the original forward source separation structure.

At convergence over several iterations, the temporal equalizer filter w _ft tends to the inverse of the quantity l-h _l2 * w _2l , with vt> 2i = ^ 2i - This last condition is ensured in practice by the use of voice activity detection on the speech signal. Indeed, according to a method known from the state of the art but advantageous for the implementation of the invention, the filter vt> ₂ i is updated only during the phases of non-vocal activity and the equalizer filter w _ft is updated only during periods of voice activity. Such an embodiment therefore ensures equalization in amplitude of the acoustic channel while preserving the same phase as the original signal.

More generally, for the time approach, as for the frequency approach described below, a voice activity detection module DAV (FIG. 11) is advantageously used to estimate a representative quantity of the noise during the non-activity phases and a representative quantity of the useful signal during the activity phases. For any audio signals, other than voice, one can provide a device of the state of the art such as a threshold detector.

This first embodiment based on a temporal adaptation nevertheless has some disadvantages. The adaptive filter w _p must be long, and its convergence is disturbed by the presence of noise superimposed on the speech in the signal pι (n). It is therefore considered that, in practice, this temporal computation approach gives insufficient performance, contrary to the approach based on the frequency calculation described hereinafter. The second approach in the sense of the invention is based on a gain calculation in the frequency domain. The second embodiment of the invention is directed to the direct gain calculation in the frequency domain, corresponding to a theoretical post-filter. In the third embodiment, still according to this "frequency" approach, a frequency adaptive algorithm is advantageously used, for example of the FLMS type (for "Frequency-domain Least Mean Squares") for calculating the post-filter. An algorithm of this type is described in particular in:

"Fast implementation of LMS adaptive filter", E.R. Ferrara, IEEE Trans.

Acoustics Speech and Signal Processing, Vol. ASSP-28, pp. 474-475, August 1980.

FIG. 8 shows a forward structure with calculation of the open loop frequency equalizer filter post-filter for the implementation of the invention according to the second aforementioned embodiment. Here, the frequency gain G (ω, k) is calculated which is used to equalize in amplitude (and not in phase) the output signal of the separation structure W ₁ (n). This gain is calculated from the unbalanced output signal and the mixing signal. It aims to restore, for each spectral component of the output signal, the same amplitude as the corresponding amplitude of the component of the speech signal present in the mixing signal p _\ (n). The power spectral densities of the signals W ₁ (n) and p _\ (n) are estimated here by means of a recursive calculation formula of the first order from the calculation of their fast Fourier transforms (or "FFT"). The calculation of the frequency gain is realized by the following formula:

where the two quantities DSP _signal and DSP _hw represent the power spectral densities estimated from the noisy original signal p _\ (n) and, respectively, from the noise-free filtered signal W ₁ Oi) on a window of several samples (or " frame "k). Advantageously, the power spectral density of the original signal is calculated during the periods of speech activity by subtracting the power spectral density of the noise, which is estimated during periods of non-speech activity, with the spectral power density of the signal. mixing mixture W ₁ (n). The property of the intermittency of the speech signal is therefore exploited to estimate the different power densities of the structure. The speech signal at the output of this structure is recovered after the modification of each frequency component of the signal W ₁ U) by the frequency gain G (ω, k). This signal is finally restored in the time domain following an inverse Fourier transform and a conventional reconstruction, for example of the "overlap-save" type described in particular in the reference Ferrara (1980) given previously.

It will be understood that the good estimate of the signal at the output of this structure is based on the good estimation of the speech signal (calculation of its power spectral density). To do this and to properly synchronize the signals at the input of the equalizer, the mixing signal can advantageously be delayed by a delay D (module z ^{~ D} of Figures 8 and 9). It is therefore preferable to ensure the correct setting of the delay parameter D for the proper functioning of this structure within the meaning of the invention. Typically, this parameter D can be set to half the size of the impulse response of the post-filter.

The third embodiment is described below with reference to FIG. 9, presenting a forward structure with calculation of the post-filter, by adaptive frequency filtering. This embodiment is based on the use of an adaptive algorithm for updating the coefficients of gain G {ω, k), calculated in the frequency domain. The signals being sampled in successive frames, for each signal frame k, an equation of the following type is provided:

G (ω, k) = G (ω, k-1) + μ (ω, k) E (ω, k) U _ι (ω, k), where:

the term G (ω, k-1) is the calculated gain for a frame k-1, preceding the current frame k,

- E (ω, k) is the frequency filtering error calculated on each frame k, given by

E (<o, k) = Pι (<o, k) - G (<o, k) Uι (<o, k),

the notation E * here symbolizes the complex conjugate number of the variable E,

and the notations P [(ω, k) and Uι (ω, k) represent the frequency components of the mixing signal and, respectively, the output signal of the forward source separation structure without post-filter.

The calculation of the adaptation step μ (ω, fc), at each frame, is typically performed according to a function which follows the rules and conventional principles of noise reduction. It can be a ratio estimate of respective power spectral densities of useful signal and noise. More particularly, this function is based on the calculation of the signal-to-noise ratio components of each frequency line. In a particular embodiment, the Wiener function is used for calculating the pitch μ (ω, k) as follows:

RSB _io (ω, k) ^'k) = ι ₊ RSB _pn MkY ⁽¹⁰⁾ where the RSB _prio quantity represents the signal-to-noise ratio, a priori, which is defined by the ratio between the estimate of the spectral density of power of the noise-cleaned speech signal and the estimated power spectral density of the noise. This signal-to-noise ratio is therefore given by a formula of the type:

DSP_signal (ω, k) RSB _prio (ω, k) =

DSP_noise (ω, k) \ The use of a variable adaptation step as a function of the signal-to-noise ratio as defined in equation (10) is advantageous because it allows a robust convergence of the adaptive frequency filter and also enables it to correct the signal distortion. of speech.

The output signal of this structure, using this adaptive filtering approach, is obtained by the relation Si (<o, k) = G (<o, k) Uι (<o, k),

Here again, the "overlap-save" processing can be applied for the reconstruction of the temporal output signal denoted S ₁ (n-D) in FIG. 9.

According to the tests carried out, the third embodiment proved to be the most robust to inaccuracies in the calculations of the spectral power densities of all the signals involved in the calculation of the filter. Thus, this third embodiment makes it possible to recover a signal close to the initial signal, which has moreover been confirmed by subjective listening.

Thus, the invention, aimed at denoising the speech signal using the forward source separation structure, allows the calculation of the theoretical post-filter regardless of the nature of the post-filter. The embodiments presented above make it possible to correct the disadvantages of the forward structure which produces a distortion of the output speech signal if it is not followed by the post-filter.

The present invention also aims at a sound acquisition module, in particular for a telecommunication equipment (for example a fixed or mobile telephony apparatus) as represented in FIG. 10. The sound acquisition module comprises at least:

a microphone MIC1 for acquiring a signal comprising a useful component and a noise component, a microphone MIC2 for acquiring a noise reference substantially correlated with the noise component of the input signal, and

- a FW noise reduction device according to the invention for supplying a useful signal s _u, free from noise and distortion.

The signal comprising the useful component is applied as an input signal of the channel comprising adaptive post-filtering within the meaning of the invention, and the noise reference is applied as an input signal in the other channel. of the forward structure of the noise reduction device.

Preferably, the two signals thus acquired (that including the aforementioned noise component and that corresponding to the noise reference) comprise respective substantially correlated versions of noise.

The present invention also aims at a noise reduction method in at least one signal, in which a forward structure is provided at least two adaptive noise reduction filter channels W ₁₂ (z), W ₂₁ (z) on two input signals

P ₁ (n), p ₂ (n), for outputting two filtered signals M ₁ (w), u ₂ (n), the signal M ₁ (") being reduced to noise. At the output of at least one channel selected from the two paths of the forward structure, a post-filtering is applied with an adaptation means according to a comparison involving the input signal p _γ (n) of said chosen channel, to reduce a distortion on the filtered signal M ₁ {n) of this chosen channel.

FIG. 11 shows the process steps for the second and for the third embodiments described above. Under the control of a voice activity detection DAV (step S100), the DSP power spectral densities (step S101) for evaluating the signal-to-noise ratio (step S102) are calculated and hence the gain G (ω, k) (step S103). In the second and third embodiments described above, a frequency gain G (ω, k) (step S103) is calculated by exploiting the aforementioned signal-to-noise ratio and, more particularly, the ratio of the spectral densities of DSP powers. respectively. To perform this step SlOl for calculating the spectral densities of DSP powers, the original input signal p \ (n) and the filtered signal W ₁ (W) are brought back to the frequency domain. For this purpose, a delay D is applied to the original input signal p _\ (n) (step S104), and then the delayed signal is returned to the frequency domain by applying an FFT (step S 105). The filtered, noise-free signal W ₁ (z) is also returned to the frequency domain by applying an FFT (step S106). Once the calculated gain G (ω, k) (step S103), by the technique of the second embodiment (frequency equalization) or by the technique of the third embodiment (frequency adaptation), the gain is applied to the filtered signal and expressed in the frequency domain W ₁ ()) (step S107 in the second as in the third embodiment). However, in the third embodiment (illustrated by dashed lines), provision is furthermore made for a subtraction (step S 108) of the original signal P ₁ (Co) expressed in the frequency domain, to schematically carry out the adaptation filtering. Finally, the signal S _u thus filtered is brought back into the time domain by an inverse FFT (step S 109).

Since the input signals can be digital, a processor of a noise reduction device, judiciously programmed, can implement the steps of the method. As such, the present invention also provides a computer program, intended to be executed by such a processor, and including instructions for the implementation of the method. Figure 11 can illustrate the flowchart of such a computer program.

Of course, the present invention is not limited to the embodiment described above by way of example; it extends to other variants. Thus, even if only two paths are represented in the forward structures of FIGS. 7 to 9, it will be understood that it is possible to provide a forward structure comprising more than two channels and / or more than one adaptive post-filtering in the sense of the invention. Furthermore, the post-filtering w _P 2 (z) on the noise reference channel of FIGS. 7 to 9 is not necessary for the implementation of the invention and could be omitted.

Claims

claims

1. A device for reducing noise in at least one signal, comprising:

a forward-type structure with at least two adaptive noise reduction filtering channels (w ₁₂ (z), w ₂₁ (z)) on two input signals (p _y (n), p ₂ (n)) , to output two filtered and noise-reduced signals (M ₁ (n), w ₂ (n)), and at least one post-filter (PF1) output from a channel selected from both channels, to reduce distortion on the filtered signal (W ₁ (w)) of said chosen channel, characterized in that the post-filter (PF1) comprises an adaptation means according to a comparison involving the input signal (p _γ (n)) of said chosen path.

2. Device according to claim 1, characterized in that the two input signals (P ₁ (n), p ₂ (n)) comprise respectively substantially correlated versions of noise.

3. Device according to claim 2, characterized in that the input signal (p _γ {n)) of said selected channel comprises a useful component, while the input signal (p ₂ (n)) of the Another way of the forward structure includes a noise reference.

4. Device according to one of claims 1 to 3, characterized in that the post-filter (PFl) comprises adaptive feedback adaptive temporal filtering according to a recurrent comparison based on the difference between the output signal (s _γ {n)) and the input signal (p _γ {n)) of said chosen channel.

5. Device according to one of claims 1 to 3, characterized in that the post-filter (PFl) comprises an open loop frequency equalizer filter matching means according to a comparison based on a spectral power density ratio, respectively between the filtered signal (W ₁ (")) and the input signal (p _γ (n)) of said selected channel, brought back to the frequency domain.

6. Device according to claim 5, wherein the input signals are sampled in successive k-frames, characterized in that the frequency equalizer filtering implements the calculation of a given gain G (ω, k) for a frame. current k, by a formula of the type:

where the quantities DSP _signal and DSP _hw represent the power spectral densities respectively estimated from the input signal (p _γ (n)) and the filtered signal (M ₁ {n)) of said chosen channel, for the current frame k.

7. Device according to one of claims 1 to 3, characterized in that the post-filter (PFl) comprises adaptive adaptive filtering adaptation feedback according to a recurrent comparison based on the difference between the output signal (^ ( w)) and the input signal (P ₁ (n)), brought back to the frequency domain.

8. Device according to claim 7, wherein the input signals are sampled in successive frames, characterized in that the adaptive frequency filtering implements the recursive calculation of a gain G (ω, k) given by a formula.

Type G (ω, k) = G (ω, k -i) + μ [ω, k) E _(^ ω, k) U _ι [ω, k), wherein: - the term G (ω, k - l) is the calculated gain for a frame k1, preceding a current frame k, - the term μ (ω, k) is an adaptation step calculated for each current frame k as a function of a ratio estimate of spectral densities of respective power of wanted signal and noise for said selected channel, the notation E ^ ω, k) symbolizes here the conjugate complex of the term E (ω, k), this term E (ω, k) representing a frequency filtering error on the current frame k, given by a formula of the type:

E (ω, fc) = P [(ω, k) ^~ G ((o, k) Uι ((o, k), where the notation P [((o, k) and Uι ((o, k) represent frequency components of the input signal and, respectively, the filtered signal of said selected channel.

9. Device according to one of claims 5 to 8, the input signals being speech signals, characterized in that it comprises a voice activity detection module (DAV) to allow the calculation of the spectral density. of noise power during voice non-activity phases and allow the calculation of the useful signal power spectral density during the speech activity phases.

10. Device according to one of claims 5 to 9, characterized in that the post-filter comprises a module (z ^{~ D} ) applying a selected delay to the input signal of the chosen channel (p _γ (n)) for determine said comparison involving the input signal

11. Device according to claim 10, characterized in that the post-filter is a finite-response filter of given length, and in that the delay (D) applied to the input signal is chosen to correspond substantially to half of the length of the postfilter.

12. Sound acquisition module, comprising at least: a microphone for acquiring a signal comprising a useful component and a noise component,

and a microphone for acquiring a noise reference substantially correlated with said noise component, characterized in that it comprises a noise reduction device according to one of the preceding claims, wherein:

the signal comprising the useful component is applied as an input signal of the selected channel of the noise reduction device, and the noise reference is applied as an input signal in the other channel of the noise reduction device; noise reduction.

13. Telecommunication equipment, characterized in that it comprises a sound acquisition module according to claim 12.

A method of reducing noise in at least one signal, wherein a forward-looking structure is provided with at least two adaptive noise reduction filtering channels (w ₁₂ (z), w ₂₁ (z)) on two signals input (/ J ₁ (^), /? ₂ (")), for outputting two filtered signals (M ₁ (n), M ₂ (n)), one of which (u \ (n)), is reduced to noise, characterized in that, at the output of at least one channel chosen from the two paths of the forward structure, post-filtering is applied with an adaptation means according to a comparison involving the input signal (P ₁ (n)) of said selected channel, to reduce distortion on the filtered signal (M ₁ (n)) of said selected channel.

Computer program, intended to be executed by a processor of a noise reduction device, characterized in that it comprises instructions for carrying out the method according to claim 14.