EP2293594B1 - Verfahren zur Filterung von seitlichem nichtstationärem Rauschen für ein Multimikrofon-Audiogerät - Google Patents

Verfahren zur Filterung von seitlichem nichtstationärem Rauschen für ein Multimikrofon-Audiogerät Download PDF

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EP2293594B1
EP2293594B1 EP10166119A EP10166119A EP2293594B1 EP 2293594 B1 EP2293594 B1 EP 2293594B1 EP 10166119 A EP10166119 A EP 10166119A EP 10166119 A EP10166119 A EP 10166119A EP 2293594 B1 EP2293594 B1 EP 2293594B1
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noise
transients
probability
speech
signal
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French (fr)
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EP2293594A1 (de
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Guillaume Vitte
Julie Seris
Guillaume Pinto
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Parrot SA
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Parrot SA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • 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
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal 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
    • G10L2021/02087—Noise filtering the noise being separate speech, e.g. cocktail party
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal 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
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2201/00—Details of transducers, loudspeakers or microphones covered by H04R1/00 but not provided for in any of its subgroups
    • H04R2201/10—Details of earpieces, attachments therefor, earphones or monophonic headphones covered by H04R1/10 but not provided for in any of its subgroups
    • H04R2201/107—Monophonic and stereophonic headphones with microphone for two-way hands free communication
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2430/00—Signal processing covered by H04R, not provided for in its groups
    • H04R2430/03—Synergistic effects of band splitting and sub-band processing

Definitions

  • the invention relates to the treatment of speech in a noisy environment.
  • microphone microphone
  • noise that is a disruptive element that can go, in some cases, to make incomprehensible the speaker's words. It is the same if one wants to implement speech recognition techniques, because it is very difficult to perform a form recognition on words embedded in a high noise level.
  • Some of these devices provide for the use of several microphones, usually two microphones, and use the average of the signals picked up, or other more complex operations, to obtain a signal with a lower level of interference.
  • a so-called beamforming technique makes it possible to create, by software means, a directivity that improves the signal-to-noise ratio, but the performances of this technique are very limited when only two microphones are used.
  • conventional techniques are especially adapted to the filtering of diffuse noise, stationary, coming from the surroundings of the device and found at comparable levels in the signals picked up by the two microphones.
  • One of the aims of the invention is to take advantage of the multi-microphone structure of the device to operate a spatial detection of these nonstationary noises, then to discriminate, among all the nonstationary components (hereinafter "transients") those which are nonstationary noise components from those which are speech components, and finally to process the captured signal to effectively denoise it while minimizing the distortions introduced by this processing.
  • transients those which are nonstationary noise components from those which are speech components
  • lateral noise a directional non-stationary noise whose direction of arrival is far from that of the useful signal
  • privileged cone the direction or angular sector of the space where the source is located of useful signal (the speech of the speaker) compared to the network of microphones.
  • the starting point of the invention 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 the speech, then to deduce a probability of presence of the speech which will serve to attenuate this noise.
  • the invention relates to a method of denoising a noisy acoustic signal picked up by a plurality of microphones of a multi-microphone audio device operating in a noisy environment.
  • the noisy acoustic signal includes a speech component derived from a directional speech source and a noise noise component, said noise component itself including a directional non-stationary side noise component.
  • the Figure 1 is a block diagram showing the different modules and functions implemented by the method of the invention as well as their interactions.
  • the method of the invention is implemented by software means, which can be broken down and schematized by a number of modules 10 to 24 illustrated Figure 1 .
  • the signal which one wishes to denoise comes from a plurality of signals picked up by a network of microphones (which, in the minimum configuration, can be simply a network of two microphones) arranged in a predetermined configuration.
  • the microphone array captures the signal transmitted by the useful signal source (speech signal), and the difference in position between the microphones induces a set of phase shifts and amplitude variations in the recording of the signals emitted by the signal source. useful.
  • n is the amplitude attenuation due to the energy loss between the position of the sound source s and the microphone
  • ⁇ n is the phase shift between the signal transmitted and received by the microphone
  • v n represents the value of the diffuse noise field at the microphone position.
  • the delays ⁇ n can then be calculated from the angle ⁇ s , defined as the angle between the mediators of the pairs of microphones (n, m) and the reference direction corresponding to the source s of useful signal.
  • the angle ⁇ s is zero.
  • the signal in the time domain x n (t) coming from each of the N micros is digitized, cut into frames of T time points, temporally windowed by a Hanning type window, then the fast Fourier transform FFT (short-term transform) X n ( k, l ) is calculated for each of these signals:
  • X not k l at not .
  • d not k e - i ⁇ 2 ⁇ ⁇ ⁇ f k ⁇ ⁇ not 1 being the index of the time frame, k being the index of the frequency band, and f k being the center frequency of the frequency band indexed by k.
  • the signals X n ( k, l ) can be combined with each other by a simple beamforming pre- filtering technique of the Delay and Sum type which is applied to obtain a partially denoised combined signal X ( k, I ):
  • X k l 1 NOT ⁇ not ⁇ 1 NOT d not k ⁇ .
  • X not k l 1 NOT ⁇ not ⁇ 1 NOT d not k ⁇ .
  • this treatment provides only a slight improvement in the signal / noise ratio, of the order of 1 dB only.
  • the angle ⁇ s is zero and it is a simple average that is made on both microphones.
  • the purpose of this step is to calculate an estimate of the pseudo-stationary noise component V ( k, l ) present on the signal X ( k, l ).
  • V ( k, l ) V ( k, l )
  • MCRA pseudo-stationary minimum recursive averaging noise component
  • Transients refers to all non-stationary signals, including both useful speech and sporadic non-stationary noises, which may have energy equivalent to or sometimes greater than useful speech (passing a vehicle, siren, horn, other people's words etc.).
  • the processing performed by the block 16 consists only in calculating a probability p Trausient ( k, l ) of presence of transient signals, without distinction between useful speech and non-stationary noise noises.
  • the algorithm is as follows:
  • TSR min and TSR max are chosen so as to correspond to typical situations, close to reality.
  • This calculation takes advantage of the fact that, unlike the pseudo-stationary component of the noise that is diffuse, the transients are often directional, that is to say from a point sound source (such as the mouth of the speaker for useful speech, or the engine of a motorcycle for a lateral noise). It is therefore advisable to calculate the direction of arrival of these signals, which will be generally well defined, and to compare this direction of arrival at the angle ⁇ s corresponding to the direction of origin (useful speech), so as to determine whether the non-stationary signal considered is useful or parasitic, and thus to discriminate between useful speech and non-stationary noise.
  • the first step is to estimate the direction of arrival of the transient.
  • the method used here is based on the use of the probability of transient ( k, 1 ) transient p- transients determined by block 18 as discussed above.
  • Each angle ⁇ i is tested to determine the one that is closest to the direction of arrival of the non-stationary signal studied. To do this, we consider each pair of microphones ( n, m ) and we calculate an estimator corresponding direction of arrival P n, m ( ⁇ i , k , l ), whose module will be maximum when the angle ⁇ i tested is closest to the direction of arrival of the transient.
  • Another method used here in a preferential way, consists in weighting the estimator P n, m ( ⁇ i , k, l ) by the probability of presence of transients p Transient ( k, l ) , and defining a new decision strategy.
  • the estimate of angle ⁇ max is not made on each frequency band, but on each packet K j of frequency bands.
  • the following step which is characteristic of the method of the invention, consists in calculating a probability of presence of speech based on the arrival direction estimation ⁇ ( k, l ) obtained in the manner indicated above.
  • the probability p spa ( k, l ) can be calculated in different ways, giving a binary value or multiple values. Two examples of calculation p spa ( k, l ) are given below , given that other laws can be used to express p spa ( k, l ) from ⁇ ( k, l ) .
  • the probability p spa ( k, l ) of the presence of speech calculated in block 20, itself dependent on the probability p Transient ( k, l ) of the presence of transients computed at block 16, will be used as input parameter in a classic technique of denoising.
  • 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.
  • the probability of presence of speech occurs at two important moments, for the estimation of the noise energy and for the calculation of the final gain, and the probability p spa ( k, l ) will be used at these two levels. .
  • the probability p spa ( k, l ) modulates the forgetting factor in the noise estimate, which is updated more rapidly on the noisy signal X ( k, l ) when the probability of speech is weak, this mechanism completely conditioning the quality of ⁇ Noise ( k, l ) .
  • G H 1 ( k, l ) being a denoising gain (whose calculation depends on the noise estimate ⁇ Noise ) described in the aforementioned article by Cohen, and G min being a constant corresponding to the denoising applied when speech is considered absent.
  • the probability p spa ( k, l ) plays a large role in the determination of G OM-LSA gain (k, l).
  • the gain is equal to G min and a maximum noise reduction is applied: if, for example, a value of 20 dB is chosen for G min , the non-stationary noises previously detected are attenuated by 20 dB.
  • This hybrid probability makes it possible to benefit from the identification of non-stationary noise associated with small values of p spa ( k, l ) , and to complete the estimation of the probability p hybrid ( k, l ) on parts ( k, l ).
  • the direction of arrival estimate ⁇ ( k, l ) has not been defined (producing a probability p spa ( k, l ) forced to the value 1 for safety).
  • the hybrid p hybrid probability ( k, l ) thus integrates both the non-stationary noises detected by p spa ( k, l ) and the other noises (for example pseudo-stationary) detected by p ( k, l ) .
  • the last step consists in applying to the signal ⁇ ( k, l ) a fast inverse Fourier transform iFFT to obtain in the time domain the denoised speech signal ⁇ ( t ) .

Claims (10)

  1. Verfahren zum Entfernen des Rauschens aus einem verrauschten akustischen Signal, das von mehreren Mikrophonen einer Mehrfachmikrophon-Audiovorrichtung aufgenommen wird, die in einem verrauschten Milieu arbeitet, insbesondere eine "Freisprech"-Telephonvorrichtung für Kraftfahrzeuge,
    wobei das verrauschte akustische Signal eine nutzbare Sprachkomponente, die von einer gerichteten Sprachquelle ausgeht, und eine parasitäre Rauschkomponente umfasst, wobei diese Rauschkomponente ihrerseits eine gerichtete, nicht stationäre Lateralrauschkomponente enthält,
    wobei das Verfahren dadurch gekennzeichnet ist, dass es im Frequenzbereich für mehrere Frequenzbänder, die für aufeinander folgende Zeitrahmen des Signals definiert sind, die folgenden Signalverarbeitungsschritte umfasst:
    a) Kombinieren (12) der mehreren Signale, die von der entsprechenden Mehrzahl von Mikrophonen aufgenommen werden, zu einem verrauschten kombinierten Signal (X(k, l));
    b) anhand des verrauschten kombinierten Signals Schätzen (14) einer pseudostationären Rauschkomponente (V̂(k, l)), die in diesem verrauschten kombinierten Signal enthalten ist;
    c) anhand der im Schritt b) geschätzten pseudostationären Rauschkomponente und des verrauschten kombinierten Signals Berechnen (16) einer Wahrscheinlichkeit (pTransient (k, l)) für das Vorhandensein von Übergängen in dem verrauschten kombinierten Signal;
    d) anhand der mehreren Signale, die von der entsprechenden Mehrzahl von Mikrophonen aufgenommen werden, und der Wahrscheinlichkeit für das Vorhandensein von Übergängen, die im Schritt c) berechnet wird, Schätzen (18) einer Hauptankunftsrichtung (θ́(k, l)) der Übergänge;
    e) anhand der Hauptankunftsrichtung der Übergänge, die im Schritt d) geschätzt wird, Berechnen (20) einer Wahrscheinlichkeit (pspa(k, l)) für das Vorhandensein von Sprache anhand eines räumlichen Kriteriums, die die Unterscheidung zwischen nutzbarer Sprache und lateralem Rauschen unter den Übergängen ermöglicht;
    f) anhand der Wahrscheinlichkeit für das Vorhandensein von Sprache, die im Schritt e) berechnet wird, und des verrauschten kombinierten Signals selektives Reduzieren (22) des Rauschens durch Anwenden einer variablen Verstärkung, die jedem Frequenzband und jedem zeitlichen Rahmen eigentümlich ist.
  2. Verfahren nach Anspruch 1, wobei die Verarbeitung des Schrittes a) eine Vorfilterungsverarbeitung des Typs Fixed Beamforming ist.
  3. Verfahren nach Anspruch 1, wobei die Verarbeitung im Schritt d) die folgenden aufeinander folgenden Unterschritte umfasst:
    d1) Aufteilen des Raums in mehrere Winkelsektoren;
    d2) für jeden Sektor Bewerten einer Schätzung der Ankunftsrichtung anhand der mehreren Signale, die durch die entsprechende Mehrzahl von Mikrophonen aufgenommen werden;
    d3) Gewichten jeder Schätzung mit der Wahrscheinlichkeit für das Vorhandensein von Übergängen, die im Schritt c) berechnet wird;
    d4) anhand der gewichteten Schätzungswerte, die im Schritt d3) berechnet werden, Schätzen einer Hauptankunftsrichtung der Übergänge;
    d5) Erklären der Schätzung der Hauptankunftsrichtung der Übergänge, die im Schritt d4) ausgeführt wird, für gültig oder ungültig.
  4. Verfahren nach Anspruch 3, wobei im Schritt d5) die Schätzung nur für gültig erklärt wird, falls der Wert der gewichteten Schätzung, der der geschätzten Richtung entspricht, größer als ein vorgegebener Schwellenwert ist.
  5. Verfahren nach Anspruch 3, wobei im Schritt d5) die Schätzung nur für gültig erklärt wird, wenn kein lokales Maximum der gewichteten Schätzung in dem Ausgangswinkelsektor des Sprach-Nutzsignals vorhanden ist.
  6. Verfahren nach Anspruch 3, wobei im Schritt d5) die Schätzung nur für gültig erklärt wird, falls der Wert der Schätzung über mehrere aufeinander folgende zeitliche Rahmen monoton zunimmt.
  7. Verfahren nach Anspruch 3, das außerdem einen Schritt des Haltens der Schätzung der Hauptankunftsrichtung während einer vorgegebenen minimalen Zeitdauer umfasst.
  8. Verfahren nach Anspruch 1, wobei die Wahrscheinlichkeit für das Vorhandensein von Sprache, die im Schritt e) berechnet wird, eine binäre Wahrscheinlichkeit ist, die einen Wert 1 oder 0 annimmt, je nachdem, ob die Hauptankunftsrichtung der Übergänge, die im Schritt d) geschätzt wird, in dem Ausgangswinkelsektor des Sprach-Nutzsignals vorhanden ist oder nicht.
  9. Verfahren nach Anspruch 1, wobei die Wahrscheinlichkeit für das Vorhandensein von Sprache, die im Schritt e) berechnet wird, eine Wahrscheinlichkeit mit mehreren Werten ist, die vom Winkelabstand zwischen der Hauptankunftsrichtung der Übergänge, die im Schritt d) geschätzt wird, und der Ausgangsrichtung des Nutz-Sprachsignals abhängt.
  10. Verfahren nach Anspruch 1, wobei die Verarbeitung des Schrittes f) eine Verarbeitung der selektiven Verringerung des Rauschens durch Anwenden einer Verstärkung mit optimierter, modifizierter logarithmischer Spektralamplitude (OM-LSA) ist.
EP10166119A 2009-07-23 2010-06-16 Verfahren zur Filterung von seitlichem nichtstationärem Rauschen für ein Multimikrofon-Audiogerät Active EP2293594B1 (de)

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FR0955133A FR2948484B1 (fr) 2009-07-23 2009-07-23 Procede de filtrage des bruits lateraux non-stationnaires pour un dispositif audio multi-microphone, notamment un dispositif telephonique "mains libres" pour vehicule automobile

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FR2948484B1 (fr) 2011-07-29
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ATE532345T1 (de) 2011-11-15
US8370140B2 (en) 2013-02-05
EP2293594A1 (de) 2011-03-09
FR2948484A1 (fr) 2011-01-28

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