EP1830349B1 - Verfahren zur Geräuschdämpfung eines Audiosignals - Google Patents
Verfahren zur Geräuschdämpfung eines Audiosignals Download PDFInfo
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- EP1830349B1 EP1830349B1 EP07290219A EP07290219A EP1830349B1 EP 1830349 B1 EP1830349 B1 EP 1830349B1 EP 07290219 A EP07290219 A EP 07290219A EP 07290219 A EP07290219 A EP 07290219A EP 1830349 B1 EP1830349 B1 EP 1830349B1
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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
Definitions
- the present invention relates to the denoising of audio signals picked up by a microphone in a noisy environment.
- the invention is advantageously applied, but in a nonlimiting manner, to the speech signals picked up by the hands-free telephones or the like.
- These devices include a sensitive microphone not only capturing the voice of the user, but also the surrounding noise, noise that is a disruptive element that can go, in some cases, to make incomprehensible the speaker's words.
- the WO-A-98/45997 uses the push-button activation of a phone (for example when the driver wants to answer an incoming call) to detect the beginning of a speech signal and consider that the signal previously received to this support was essentially a noise signal. This last signal, stored, is analyzed to give a weighted average energy spectrum of the noise, then subtract from the noisy speech signal.
- the US-A-5,742,694 describes another technique, implementing a predictive adaptive filter type mechanism.
- This filter delivers a "reference signal” corresponding to the predictable part of the noisy signal and an "error signal” corresponding to the prediction error, then attenuates these two signals in variable proportions, and recombines them to provide a signal noised.
- Still other techniques called beamforming or double-phoning , implement two separate microphones.
- the first is designed and placed to primarily capture the speaker's voice, while the other is designed and placed to capture a larger noise component than the main microphone.
- the comparison of the signals captured makes it possible to extract the voice of the ambient noise efficiently, and by relatively simple software means.
- This technique based on a spatial coherence analysis of two signals, however, has the disadvantage of requiring two remote microphones, which generally confines it to fixed or semi-fixed installations and does not allow to integrate it into a pre-existing device by simply adding a software module. It also assumes that the speaker's position relative to the two microphones is approximately constant, which is generally the case in a car phone used by its driver. In addition, to achieve a near satisfactory denoising, the signals are subjected to a significant pre-filtering, which again has the disadvantage of introducing distortions that degrade the quality of the denoised signal restored.
- the invention relates to a technique for denoising audio signals picked up by a single microphone recording a voice signal in a noisy environment.
- the application of a gain called gain LSA makes it possible to minimize the mean square distance between the logarithm of the amplitude of the estimated signal and the logarithm of the amplitude of the original speech signal.
- This second criterion is superior to the first because the distance chosen is much better suited to the behavior of the human ear and therefore qualitatively gives better results.
- the essential idea is to reduce the energy of the very noisy frequency components by applying a low gain while leaving intact (by the application of a gain equal to 1) those that are little or no at all.
- the knowledge of the indices of the frames where the speech is absent makes it possible to evaluate the power of the noise as well as its evolution over time on this segment of the spectrum. It suffices to measure the energy of the raw signal when the speech is absent and to make an average continuously updated these measurements. The main question is therefore when exactly the speech of the speaker is absent from the signal picked up by the microphone.
- the method described in this article is not intended to identify precisely on which frequency components of which frames the speech is absent, but rather to give a confidence index between 0 and 1, a value of 1 indicating that the speech is absent for sure (according to the algorithm) while a value 0 declares the opposite.
- this index is likened to the probability of absence of speech a priori , ie the probability that speech is absent on a given frequency component of the frame considered. This is of course a non-rigorous assimilation in the sense that even if the presence of speech is probabilistic ex ante, the signal picked up by the microphone can at any moment only go through two distinct states. It can either (at the moment considered) include speech or not contain it.
- One of the aims of the invention is to overcome the drawbacks of the methods proposed up to now, by means of an improved denoising method applicable to a speech signal considered in isolation, in particular a signal picked up by a single microphone, a method which is based on the analysis of the temporal coherence of the captured signals.
- the starting point of the invention lies in the observation that speech generally has a temporal coherence greater than noise and that, as a result, it is clearly more predictable.
- the invention proposes to use this property to calculate a reference signal where the speech has been more attenuated than the noise, by applying in particular a predictive algorithm which may for example be of the LMS ( Least Mean Squares, Least Mean Squares ) type. ).
- This reference signal derived from the speech signal to be denoised may be used in a manner comparable to that of the signal of the second microphone of beam-forming techniques. two-way, for example techniques similar to those of Cohen and Berdugo [4, supra].
- the calculation of a ratio between the respective energy levels of the original signal and the reference signal thus obtained will make it possible to discriminate between the speech components and the nonstationary noise noises, and will provide an estimate of the probability of presence of speech of independently of any statistical model.
- the technique proposed by the invention implements an "intelligent subtraction” implying, after a linear prediction made on the passed samples of the original signal (and not of a prefiltered signal, thus degraded), a registration phase between the original signal and the predicted signal.
- the technique of the invention turns out, in practice, sufficiently powerful to provide extremely effective denoising directly on the original signal, freeing distortions introduced by a prefiltering chain, become unnecessary.
- the predictive algorithm is advantageously a recursive adaptive algorithm of LMS mean least squares type.
- Step c) advantageously comprises the application of a variable gain algorithm depending on the probability of presence / absence of speech, in particular an OM-LSA optimized modified log-spectral amplitude gain type algorithm.
- the signal that we want to denoise is a sampled digital signal x (n) , where n denotes the number of the sample ( n is the temporal variable).
- the noisy signal x (n) is applied as input to a predictive LMS algorithm schematized by block 10, including the application of appropriate delays 12.
- a predictive LMS algorithm schematized by block 10 including the application of appropriate delays 12. The operation of this LMS algorithm will be described below, with reference to FIG. figure 2 .
- the short-term Fourier transform of the captured signal x (n) (block 16) and the signal y (n) delivered by the predictive LMS algorithm (block 14) are then calculated. From these two transforms is calculated a reference signal (block 18), which is one of the input variables of an algorithm for calculating the probability of absence of speech (block 24). Meanwhile, the noisy signal transform x (n), from block 16, is also applied to the probability calculation algorithm.
- Blocks 20 and 22 estimate the pseudo-stationary noise of the reference signal and the noisy signal transform is estimated, and the result is also applied to the probability calculation algorithm.
- the result of the speech absence probability calculation, as well as the noisy signal transform, are inputted to an OM-LSA gain processing algorithm (block 26), the result of which is subjected to an inverse transformation of Fourier (block 28) to give an estimate of speech de-noiseed.
- the predictive algorithm LMS (block 10) is schematized on the figure 2 .
- ⁇ i ⁇ not + 1 ⁇ i not + 2 ⁇ ⁇ not ⁇ x ⁇ not - ⁇ - i + 1 ⁇ being a gain constant which makes it possible to adjust the speed and the stability of the adaptation.
- the respective signals x (n) and y (n) (noisy speech signal and linear prediction) are split into frames of identical lengths, and their short-term Fourier transform (denoted respectively X and Y ) is calculated for each frame.
- the algorithm predicts a 50% overlap between consecutive frames, and the samples are multiplied by the coefficients of the Hanning window so that the addition of even and odd fields corresponds to the signal of origin itself.
- E ⁇ Ref k ⁇ l 2 E ⁇ S k ⁇ l 2 ⁇ ⁇ S k + E ⁇ D t k ⁇ l 2 ⁇ ⁇ D t k + E ⁇ D ps k ⁇ l 2 ⁇ ⁇ D ps k or ⁇ S k ⁇ ⁇ D t k ⁇ ⁇ D ps k represent the attenuation on the reference signal of the three signals in each spectrum segment.
- S being a smoothed estimate of the instantaneous energy:
- M being an estimator of the pseudo-stationary energy, which can be obtained for example by a method MCRA ( Minima Controlled Recursive Averaging ) of the same type as that described by Cohen and Berdugo [5, supra] (however, several alternatives exist in the literature).
- L x and L Ref are transient detection thresholds.
- ⁇ min (k) and ⁇ m ax (k) are the upper and lower limits for each spectrum segment. These various parameters are chosen so as to correspond to typical situations, close to reality.
- the next step (corresponding to block 26 of the figure 1 ) consists in operating the denoising itself (reinforcement of the speech component).
- the estimator just described will be applied to the statistical model described by Ephraim and Malah [2, supra], which assumes that the noise and speech in each spectrum segment are independent Gaussian processes of respective variances ⁇ x ( k, l) and ⁇ d (k, l) .
- This step may advantageously implement the OM-LSA gain algorithm ( Optimally Modified Log-Spectral Amplitude Gain ) described by Cohen and Berdugo [3, cited above].
- the G min gain in the absence of speech hypothesis is a lower limit for noise reduction, in order to limit the distortion of speech.
- the signal obtained at the end of this treatment is subjected to an inverse Fourier transform (block 28) to give the final estimate of the denoised speech.
- the algorithm of the present invention is particularly effective in noisy environments, parasitized by both mechanical noises, vibrations, etc. as well as by musical noises, characteristic situations encountered in the interior of a car. Spectrograms show that the attenuation of the noise is not only effective, but is done without significant distortion of speech after denoising.
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- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Soundproofing, Sound Blocking, And Sound Damping (AREA)
- Signal Processing Not Specific To The Method Of Recording And Reproducing (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
- Noise Elimination (AREA)
Claims (8)
- Verfahren zum Verarbeiten eines Audiosignals zum Entfernen des Rauschens aus einem ursprünglichen verrauschten Signal, das eine mit einer Rauschkomponente kombinierte Sprachkomponente enthält, wobei diese Rauschkomponente ihrerseits eine transistorische Rauschkomponente und eine pseudostationäre Rauschkomponente enthält,
dadurch gekennzeichnet, dass dieses Verfahren ein Verfahren für die Analyse der zeitlichen Kohärenz des abgetasteten verrauschten Signals ist, das die folgenden Schritte umfasst:a) Bestimmen eines Referenzsignals durch Anwenden einer Verarbeitung (10, 18), die die Sprachkomponenten stärker als die Rauschkomponenten dieses verrauschten Signals dämpfen kann, auf das verrauschte Signal, wobei die Verarbeitung Folgendes umfasst:a1) Anwenden eines adaptiven linearen Prädiktionsalgorithmus auf eine Linearkombination von früheren Abtastwerten des verrauschten Signals unda2) Bestimmen des Referenzsignals durch eine Subtraktion mit Phasenverschiebungsausgleich zwischen dem nicht vorgefilterten, ursprünglichen verrauschten Signal und dem von dem linearen Prädiktionsalgorithmus gelieferten Signal;b) Bestimmen (24) einer Wahrscheinlichkeit für das Vorliegen/Fehlen von Sprache a priori anhand jeweiliger Energieniveaus in dem Spektralbereich des verrauschten Signals und des Referenzsignals; undc) Verwenden dieser Wahrscheinlichkeit für das Fehlen von Sprachen a priori, um ein Rauschspektrum zu schätzen und um aus dem verrauschten Signal eine vom Rauschen befreite Schätzung des Sprachsignals abzuleiten (26). - Verfahren nach Anspruch 1, wobei das Referenzsignal durch Anwenden einer Beziehung des Typs
im Schritt a2) bestimmt wird, wobei X(k,l) und Y(k,l) kurzfristige Fourier-Transformationen jedes Spektrumsegments k jedes Rahmens l des ursprünglichen verrauschten Signals bzw. des durch den linearen Prädiktionsalgorithmus gelieferten Signals sind. - Verfahren nach Anspruch 1, wobei der lineare Prädiktionsalgorithmus (10) ein Algorithmus des Typs mittlerer kleinster Quadrate, LMS, ist.
- Verfahren nach Anspruch 1, wobei der lineare Prädiktionsalgorithmus (10) ein rekursiver adaptiver Algorithmus ist.
- Verfahren nach Anspruch 1, wobei der Schritt b) das Anwenden eines Algorithmus für die Schätzung der Energie der pseudostationären Rauschkomponente in dem Referenzsignal und in dem verrauschten Signal umfasst.
- Verfahren nach Anspruch 5, wobei der Algorithmus zum Schätzen der Energie der pseudostationären Rauschkomponente ein Algorithmus des Typs mit rekursiver Durchschnittsbildung durch Steuerung der Minima, MRCA, ist.
- Verfahren nach Anspruch 1, wobei der Schritt c) das Anwenden eines Algorithmus für variable Verstärkung als Funktion der Wahrscheinlichkeit für das Vorhandensein/Fehlen von Sprache umfasst.
- Verfahren nach Anspruch 7, wobei der Algorithmus mit variabler Verstärkung ein Algorithmus des Typs Verstärkung mit optimierter modifizierter log-Spektralamplitude, OM-LSA, ist.
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR0601822A FR2898209B1 (fr) | 2006-03-01 | 2006-03-01 | Procede de debruitage d'un signal audio |
Publications (2)
Publication Number | Publication Date |
---|---|
EP1830349A1 EP1830349A1 (de) | 2007-09-05 |
EP1830349B1 true EP1830349B1 (de) | 2011-11-30 |
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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EP07290219A Active EP1830349B1 (de) | 2006-03-01 | 2007-02-21 | Verfahren zur Geräuschdämpfung eines Audiosignals |
Country Status (6)
Country | Link |
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US (1) | US7953596B2 (de) |
EP (1) | EP1830349B1 (de) |
AT (1) | ATE535905T1 (de) |
ES (1) | ES2378482T3 (de) |
FR (1) | FR2898209B1 (de) |
WO (1) | WO2007099222A1 (de) |
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- 2007-02-27 WO PCT/FR2007/000347 patent/WO2007099222A1/fr active Application Filing
Also Published As
Publication number | Publication date |
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US20070276660A1 (en) | 2007-11-29 |
FR2898209B1 (fr) | 2008-12-12 |
EP1830349A1 (de) | 2007-09-05 |
ATE535905T1 (de) | 2011-12-15 |
US7953596B2 (en) | 2011-05-31 |
WO2007099222A1 (fr) | 2007-09-07 |
ES2378482T3 (es) | 2012-04-13 |
FR2898209A1 (fr) | 2007-09-07 |
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