EP2538409B1 - Procédé de débruitage pour équipement audio multi-microphones, notamment pour un système de téléphonie "mains libres" - Google Patents
Procédé de débruitage pour équipement audio multi-microphones, notamment pour un système de téléphonie "mains libres" Download PDFInfo
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- EP2538409B1 EP2538409B1 EP12170874.7A EP12170874A EP2538409B1 EP 2538409 B1 EP2538409 B1 EP 2538409B1 EP 12170874 A EP12170874 A EP 12170874A EP 2538409 B1 EP2538409 B1 EP 2538409B1
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- 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
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- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R3/00—Circuits for transducers, loudspeakers or microphones
- H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones
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- 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
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- 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
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- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
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- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
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- G10L25/78—Detection of presence or absence of voice signals
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- H04R2201/00—Details of transducers, loudspeakers or microphones covered by H04R1/00 but not provided for in any of its subgroups
- H04R2201/40—Details of arrangements for obtaining desired directional characteristic by combining a number of identical transducers covered by H04R1/40 but not provided for in any of its subgroups
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- H04R2499/10—General applications
- H04R2499/13—Acoustic transducers and sound field adaptation in vehicles
Definitions
- the invention relates to the treatment of speech in a noisy environment.
- microphones sensitive not only to the voice of the user, but also capturing the surrounding noise and the echo due to the phenomenon of reverberation by the environment, typically the passenger compartment of the vehicle .
- the useful component (the speech signal of the near speaker) is thus embedded in a parasitic component of noise (external noises and reverberation) that can go, often, to make incomprehensible to the distant speaker (the one who is at the other end of the voice signal transmission path) the words of the nearby speaker.
- Some of these devices provide for the use of multiple microphones and use the average of the picked-up signals, or other more complex operations, to obtain a signal with a lower level of interference.
- so-called beamforming techniques make it possible to create, by software means, a directivity which improves the signal / noise ratio.
- the performances of this technique are very limited when only two microphones are used (concretely, it is estimated that such a method only gives good results if condition of having a network of at least eight microphones). The performance is also very degraded when the environment is reverberant.
- the aim of the invention is to propose a solution for denoising the audio signals picked up by such a multichannel, multi-microphone system, in a very noisy and very reverberant environment, typically the passenger compartment of a car.
- the main difficulty related to the methods of speech processing by multichannel systems is the difficulty of estimating useful parameters for the treatments to be applied, because the estimators are strongly related to the ambient environment.
- the EP 2 293 594 A1 (Parrot SA) describes a method for the spatial detection and filtering of nonstationary and directional noises such as horn blasts, passing a scooter, overtaking by a car, etc.
- the proposed technique consists in associating the properties of temporal and frequency non-stationarity, on the one hand, and spatial directivity, on the other hand, to detect a type of noise that is usually difficult to discriminate from speech. , in order to ensure an efficient filtering of this noise and to deduce otherwise a probability of presence of speech which will further improve the attenuation of the noise.
- the EP 2 309 499 A1 (Parrot SA) describes a system with two microphones operating a spatial coherence analysis of the signal picked up in order to determine a direction of incidence.
- the system calculates two noise references according to different methods, one according to the spatial coherence of the signals picked up (which integrates non-stationary non-directional noise) and another according to the main direction of incidence of the signals (which especially integrates directive nonstationary noises).
- This denoising technique is based on the hypothesis that speech generally has a higher spatial coherence than noise and that, moreover, the direction of speech incidence is generally well defined and can be assumed to be known: in the case of motor vehicle, it is defined by the position of the driver, to which the microphones are turned.
- the denoised signal obtained at the output satisfactorily reproduces the amplitude of the initial speech signal, but not its phase, which can cause a distortion of the voice reproduced by the device.
- the problem of the invention is to take into account a reverberant environment that does not make it possible to satisfactorily calculate a direction of arrival of the useful signal and, alternatively, to obtain a denoising which restores both the amplitude and phase of the initial signal, thus not distorting the voice of the speaker when it is reproduced by the device.
- the method of the invention is a denoising method for a device comprising a network formed of a plurality of microphone sensors arranged in a predetermined configuration.
- the calculation of the optimal linear projector of the step d) is carried out by a beamforming processing of Capon with a minimum variance response without distortion MVDR.
- step e) is effected by OM-LSA optimized modified log-spectral amplitude gain processing.
- the estimation of the transfer function of step c) is performed by calculating an adaptive filter to cancel the difference between the signal collected by the sensor whose evaluation is to be evaluated. transfer function and the signal collected by the sensor of the useful signal reference, with modulation by the probability of presence of speech.
- the adaptive filter can in particular be a LMS mean linear least linear prediction algorithm and the speech presence probability modulation, a variation modulation of the iteration pitch of the adaptive filter.
- the spectrum of the signal to be denoised is advantageously divided into a plurality of distinct spectrum parts, the sensors being grouped into a plurality of subnetworks each associated with one of the parts of the spectrum.
- the denoising process is then operated in a differentiated manner, for each part of the spectrum, on the signals collected by the sensors of the sub-network corresponding to the part of the spectrum considered.
- the spectrum of the signal to be denoised can be divided into a low frequency portion and a high frequency portion.
- the denoising processing steps are then performed only on the signals collected by the sensors furthest away from the network.
- step c) it is also possible, again with a signal spectrum to be denoised divided into a plurality of distinct spectrum parts, to differentially estimate, in step c), the transfer function of the acoustic channels by applying different treatments to each of the parts of the spectrum.
- the sensor array is a linear array of aligned sensors and the sensors are grouped into a plurality of sub-arrays each associated with one of the parts of the spectrum: for the low frequency part, the processing of denoising is performed only on the signals collected by the sensors furthest away from the network and the estimation of the transfer function is performed by calculating an adaptive filter ; and for the high frequency part, the denoising process is performed on the signals collected by all the sensors of the network, and the estimation of the transfer function is performed by a diagonalization processing.
- each sensor can be likened to a single microphone M 1 ... M n capturing a reverberated version of a speech signal emitted by a useful signal source S (the speech of a near speaker 10), which signal is added a noise.
- x i is the signal picked up
- h i being the impulse response between the useful signal source S and the sensor M i
- s being the useful signal produced by the source S (speech signal of the near speaker 10)
- b i being the additive noise
- the proposed technique consists, on the basis of the elements that have just been described, to search in the time domain for an optimal linear projector for each frequency.
- projector means an operator corresponding to a transformation of a plurality of signals, collected concurrently by a multichannel device, into a single single-channel signal.
- This projection is an "optimal" linear projection in that the residual noise component on the single-channel signal output is minimized (noise and reverberation) and that the useful speech component is the least deformed possible.
- R n is the correlation matrix between the microphones, for each frequency, and H being the acoustic channel considered.
- a first technique consists of using an LMS type algorithm in the frequency domain.
- one of the channels will be taken as a useful signal reference, for example the channel of the microphone M 1 , and the transfer functions H 2 ... H n will be calculated for the others. canals.
- the reverberation (thus parasitized) version of the speech signal S picked up by the microphone M 1 is taken as a useful signal reference, the presence of the reverberation in the signal picked up is not a problem because stage one seeks to operate a denoising and not a de-reverberation.
- the LMS algorithm aims (in known manner) to estimate a filter H (block 14) by means of an adaptive algorithm, corresponding to the signal x i delivered by the microphone M i , by estimating the transfer of noise between the microphone M i and the microphone M i (taken as reference).
- the output of the filter 14 is subtracted at 16 from the signal x 1 picked up by the microphone M 1 to give a prediction error signal allowing iterative adaptation of the filter 14. It is thus possible to predict from the signal x i the speech component (reverberated) contained in the signal x 1 .
- the signal x 1 is slightly delayed (block 18). .
- an element 20 for weighting the error signal of the adaptive filter 14 by the probability of presence of speech p delivered at the output of the block 22 is added: it is a matter of adapting the filter only when the probability of presence of speech is high. This weighting can be made in particular by modifying the adaptation step as a function of the probability p .
- H i ⁇ k + 1 H i k + ⁇ ⁇ X ⁇ k 1 T ⁇ X ⁇ k 1 - H ⁇ k i ⁇ X ⁇ k i
- Another possible technique for estimating the acoustic channel is to operate by matrix diagonalization.
- R not ⁇ k + 1 ⁇ ⁇ R not k + 1 - ⁇ ⁇ XX T ⁇ being a forgetting factor (fixed, since we take into account the entire signal).
- MSC f sinc 2 fd vs f being the frequency considered, d being the distance between the sensors, and where c is the speed of sound.
- the distance of the microphones which makes it possible to decorrelate the noises, however has the disadvantage of being translated, in the spatial field, to a sampling at a lower frequency, with the consequence of a folding of the high frequencies, which will be less well restored. .
- the invention proposes to solve this difficulty by selecting different sensor configurations according to the frequencies processed.
- the Figure 5 is a block diagram showing the different steps of signal processing from a linear array of four microphones M 1 ... M 4 such as that illustrated Figure 4 .
- the treatment that will be described is applied in the frequency domain, at each frequency bin , that is to say for each frequency band defined for the successive time frames of the signal collected by the microphones (the four microphones M 1 , M 2 , M 3 and M 4 for the high of the spectrum HF, and the two microphones M 1 and M 4 for the low of the spectrum BF).
- These signals correspond, in the frequency domain, vectors X 1 ... X n ( X 1 , X 2 , X 3 and X 4 and X 1 , X 4 , respectively).
- a block 22 produces from the signals collected by the microphones a probability p of presence of speech. As indicated above, this estimation is carried out according to a technique that is itself known, for example that described in FIG. WO 2007/099222 A1 , which can be referred to for more details.
- Block 44 schematizes a selector of the acoustic channel estimation method, ie by diagonalization on the basis of the signals collected by the four microphones M 1 , M 2 , M 3 and M 4 (block 28 of FIG. Figure 5 , for the high frequency spectrum HF), or by adaptive filter LMS on the basis of the signals collected by the two extreme microphones M 1 and M 4 (block 38 of the Figure 5 , for the low end of the BF spectrum).
- Block 46 corresponds to the estimate of the spectral noise matrix, designated R n , used for the calculation of the optimal linear projector, and also used for the diagonalization calculation of block 28 when the transfer function of the acoustic channel is estimated from this way.
- Block 48 corresponds to the calculation of the optimal linear projector.
- the projection calculated at 48 is an optimal linear projection, in that the residual noise component on the single channel signal output is minimized (noise and reverberation).
- the optimal linear projector has the particularity of recalibrating the phases of the different input signals, which makes it possible to obtain at the output a projected signal S pr which returns to the phase of the initial speech signal of the speaker (and also the amplitude of this signal, of course).
- the final step (block 50) consists of selectively reducing the noise by applying a variable gain specific to each frequency band and each time frame to the projected signal S pr .
- This denoising is also modulated by the probability of speech presence p.
- the signal S HF / BF outputted by the denoising block 50 will then undergo a fast inverse Fourier transform iFFT (blocks 30, 40 of the Figure 5 ) to obtain in the time domain the denoised speech signal S HF or S BF sought giving, after reconstruction of the complete spectrum, the final denoised speech signal s.
- iFFT fast inverse Fourier transform
- LSA Log-Spectral Amplitude
- the "OM-LSA” Optimally-Modified Log-Spectral Amplitude ) algorithm improves the calculation of the LSA gain to be applied by weighting it by the conditional probability of presence of speech p .
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- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Health & Medical Sciences (AREA)
- Otolaryngology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
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Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR1155377A FR2976710B1 (fr) | 2011-06-20 | 2011-06-20 | Procede de debruitage pour equipement audio multi-microphones, notamment pour un systeme de telephonie "mains libres" |
Publications (2)
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EP2538409A1 EP2538409A1 (fr) | 2012-12-26 |
EP2538409B1 true EP2538409B1 (fr) | 2013-08-28 |
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EP12170874.7A Active EP2538409B1 (fr) | 2011-06-20 | 2012-06-05 | Procédé de débruitage pour équipement audio multi-microphones, notamment pour un système de téléphonie "mains libres" |
Country Status (4)
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US (1) | US8504117B2 (zh) |
EP (1) | EP2538409B1 (zh) |
CN (1) | CN102855880B (zh) |
FR (1) | FR2976710B1 (zh) |
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FR2992459B1 (fr) * | 2012-06-26 | 2014-08-15 | Parrot | Procede de debruitage d'un signal acoustique pour un dispositif audio multi-microphone operant dans un milieu bruite. |
US10540992B2 (en) * | 2012-06-29 | 2020-01-21 | Richard S. Goldhor | Deflation and decomposition of data signals using reference signals |
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WO2014032738A1 (en) * | 2012-09-03 | 2014-03-06 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Apparatus and method for providing an informed multichannel speech presence probability estimation |
US9257132B2 (en) * | 2013-07-16 | 2016-02-09 | Texas Instruments Incorporated | Dominant speech extraction in the presence of diffused and directional noise sources |
BR112016012162B1 (pt) * | 2013-11-29 | 2022-09-27 | Huawei Technologies Co., Ltd | Método para reduzir sinal de autointerferência em sistema de comunicações, e aparelho |
US9544687B2 (en) * | 2014-01-09 | 2017-01-10 | Qualcomm Technologies International, Ltd. | Audio distortion compensation method and acoustic channel estimation method for use with same |
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JP6134078B1 (ja) * | 2014-03-17 | 2017-05-24 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | ノイズ抑制 |
CN105681972B (zh) * | 2016-01-14 | 2018-05-01 | 南京信息工程大学 | 线性约束最小方差对角加载的稳健频率不变波束形成方法 |
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CN110088834B (zh) * | 2016-12-23 | 2023-10-27 | 辛纳普蒂克斯公司 | 用于语音去混响的多输入多输出(mimo)音频信号处理 |
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US8380497B2 (en) * | 2008-10-15 | 2013-02-19 | Qualcomm Incorporated | Methods and apparatus for noise estimation |
FR2948484B1 (fr) | 2009-07-23 | 2011-07-29 | Parrot | Procede de filtrage des bruits lateraux non-stationnaires pour un dispositif audio multi-microphone, notamment un dispositif telephonique "mains libres" pour vehicule automobile |
FR2950461B1 (fr) * | 2009-09-22 | 2011-10-21 | Parrot | Procede de filtrage optimise des bruits non stationnaires captes par un dispositif audio multi-microphone, notamment un dispositif telephonique "mains libres" pour vehicule automobile |
CN101916567B (zh) * | 2009-11-23 | 2012-02-01 | 瑞声声学科技(深圳)有限公司 | 应用于双麦克风系统的语音增强方法 |
CN101894563B (zh) * | 2010-07-15 | 2013-03-20 | 瑞声声学科技(深圳)有限公司 | 语音增强的方法 |
-
2011
- 2011-06-20 FR FR1155377A patent/FR2976710B1/fr not_active Expired - Fee Related
-
2012
- 2012-06-05 US US13/489,214 patent/US8504117B2/en active Active
- 2012-06-05 EP EP12170874.7A patent/EP2538409B1/fr active Active
- 2012-06-19 CN CN201210202063.6A patent/CN102855880B/zh active Active
Also Published As
Publication number | Publication date |
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CN102855880A (zh) | 2013-01-02 |
FR2976710B1 (fr) | 2013-07-05 |
US8504117B2 (en) | 2013-08-06 |
CN102855880B (zh) | 2016-09-28 |
FR2976710A1 (fr) | 2012-12-21 |
EP2538409A1 (fr) | 2012-12-26 |
US20120322511A1 (en) | 2012-12-20 |
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