EP0948237B1 - Procédé pour la suppression du bruit dans un signal de microphone - Google Patents

Procédé pour la suppression du bruit dans un signal de microphone Download PDF

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Publication number
EP0948237B1
EP0948237B1 EP99106123A EP99106123A EP0948237B1 EP 0948237 B1 EP0948237 B1 EP 0948237B1 EP 99106123 A EP99106123 A EP 99106123A EP 99106123 A EP99106123 A EP 99106123A EP 0948237 B1 EP0948237 B1 EP 0948237B1
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Prior art keywords
signal
time
filtering function
microphone
frequency domain
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Expired - Lifetime
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EP99106123A
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German (de)
English (en)
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EP0948237A2 (fr
EP0948237A3 (fr
Inventor
Hans-Jörg Thomas
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Harman Becker Automotive Systems GmbH
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Harman Becker Automotive Systems GmbH
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    • 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
    • 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
    • H04R3/007—Protection circuits for transducers
    • 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
    • G10L2021/02168—Noise filtering characterised by the method used for estimating noise the estimation exclusively taking place during speech pauses

Definitions

  • the invention relates to a method for noise immunity of a microphone signal.
  • a particular situation is often given in vehicles in that a playback device such as a radio, a cassette or CD player via a loudspeaker Sound environment generates, which superimposed as an interference signal recorded by a microphone voice signal, for example, for speech recognition or telephone transmission.
  • a microphone voice signal for example, for speech recognition or telephone transmission.
  • the microphone signal must be freed as much as possible of interference signal components.
  • the interference signal emanating from a source of interference not only reaches the microphone on the shortest direct path, but also occurs via numerous reflections as a superposition of a plurality of echoes with different transit times in the microphone signal.
  • the entire effect of the interference signal from the source of interference on the microphone signal can be described by an a priori unknown transfer function of the room, such as the passenger compartment of a motor vehicle.
  • the transfer function changes depending on the occupation of the vehicle and the position of the individual persons.
  • a compensation signal can be generated which, by subtraction from the microphone signal, delivers a signal freed from the interference signal, for example a pure speech signal.
  • said replica represents a more or less good approximation to the unknown transfer function and the disturbance can not be completely eliminated.
  • EP 0 250 048 A1 discloses a digital block adaptable filter which is adapted in the frequency domain.
  • Object of the present invention is to provide a method for noise immunity of a microphone signal that has good characteristics hinsichltich the suppression with reasonable signal processing overhead.
  • the compensation of the Störsignalanteils in the microphone signal is made by means of a generated from the reference signal on the replica of the transfer function compensation signal in the frequency domain, so that the microphone signal, compensation signal and output signal in the frequency range, i. in the form of spectra.
  • the signal processing in this process step in the frequency domain requires a spectral transformation of the microphone signal, but takes into account that the reproduction of the transfer function in the frequency range is more advantageous and provides for a beneficial subsequent additional noise reduction of the output signal, which is typically also made in the frequency domain, already a particularly suitable Signal form ready.
  • an advantageous development of the invention provides for a division of the replica filter into a plurality of sub-filters at time-shifted segments of the segmented reference signal whose coefficient update can be staggered over time, whereby the signal processing effort can be minimized.
  • the division of the replica filter into a plurality of sub-filters and the noise removal on the basis of a filter setting obtained in a speech break are independent of the Störsignalkompensation in the frequency domain independently for the noise immunity of a microphone signal feasible and advantageous.
  • Fig. 1 represents the principle of a device for (single-channel) radio signal compensation.
  • the acoustic signal emitted by the loudspeaker reaches the microphone of the speech input system directly, but also via numerous reflections in the vehicle interior.
  • the transmission path G thus represents a transversal filter with a weighted sum of time-delayed echoes
  • the loudspeaker signal x is filtered by the a priori unknown transfer function G of the vehicle interior.
  • the result is the noise component r, which adds to the speech signal s to the microphone signal y.
  • an estimate r ⁇ is generated from the loudspeaker signal x by means of the filter simulation H.
  • the speech signal may still contain disturbances in the form of, for example, engine noise or external noise, but which are not explicitly dealt with in this context.
  • the H is an adaptive filter and works according to a standard method known in the literature, the LMS algorithm (Least Mean Squares).
  • the error signal E is still required in order to accomplish the coefficient adaptation in the filter H.
  • the output signal s ⁇ is fed to the determination of the filter coefficients.
  • Fig. 2a shows in another representation again the arrangement of Fig. 1 as a radio signal compensation.
  • the adaptive system H can be realized, for example, in the time domain as an FIR filter (finite-impulse-response filter). For large impulse response lengths, as they often occur in practice, however, this requires a very high computational effort.
  • FIR filter finite-impulse-response filter
  • FLMS frequency domain
  • Fig. 2b shows a block diagram of the FLMS algorithm.
  • F is a spectral transformation FFT of a time signal into the frequency domain and F -1 is the inverse IFFT.
  • the processing steps referred to as projections P1, P2 and P3 are used for the correct segmentation of the data by the block use with the FFT or IFFT and will be explained in more detail later.
  • the operation of the filter is to multiply the reference spectrum X by the filter coefficient vector H.
  • the spectrum of the filter output R ⁇ is transformed back into the time domain via F -1 .
  • the signal r ⁇ is available.
  • the projection P1 which is particularly complicated here with two spectral transformations, calculates from H 'the coefficient vector H required for the filtering.
  • the spectrum S 1 of the output signal evaluated with P 3 is calculated to calculate the correction vector ⁇ H' s + r - r ⁇ needed.
  • FIG. 3 A detailed block diagram of the in Fig. 2b shown FLMS algorithm Fig. 3 ,
  • the samples of a signal and the nodes of the FFT are commonly referred to as samples. All spectral transforms and their inverses are to be segmented as 256-point FFTs, each overlapping 128 samples.
  • the output signal s ⁇ in the time domain consists of 128 sample blocks. It arises from the difference of the second block halves (thus in each case the samples 129 to 256) of microphone signal and filtered compensation signal r ⁇ .
  • the projection P1 which requires 2 FFTs and converts the vector H 'into the vector H, is elaborate.
  • the first half (samples 1 to 128) is cut out of the complex 256-point result vector of the inverse transformation from the frequency to the time domain (IFFT) and the second half (samples 129 to 256) is set to zero.
  • the transformation into the frequency domain takes place again by means of FFT.
  • Simple is the projection P2. It consists of the above-described fragmentation of the last 128 samples, resulting in overlapping 256-sample blocks again resulting in non-overlapping 128-sample blocks.
  • the projection P3 is also very simple, which, in turn, provides overlapping 256-sample blocks from non-overlapping 128-sample blocks of the output signal by preprogramming 128 null values.
  • the adaptation of the filter coefficients H ' L + 1 for a cycle L + 1 consists of the addition of a renewal vector ⁇ H' L to the old coefficient vector H ' L.
  • the operation of the LMS algorithm is significantly influenced by the adaptation constant ⁇ and the smoothing constant ⁇ . Latches in recursion loops are labeled Sp.
  • the previously described arrangement of the FLMS algorithm allows filter emulations with a maximum impulse response length of half an FFT length, in the example case 128 samples. If longer impulse responses are to be compensated, the already known FLMS algorithm for a sub-filter ( Fig. 4a ) to n subfilters.
  • the im Fig. 4a Block B with the input signals X and S ⁇ and the compensation spectrum R ⁇ as the output is denoted by the Fig. 4b to replace the extension shown.
  • the spectrum X of the reference signal is delayed by latches D by 1 or 2 block lengths, and the instantaneous X1 and the two delayed spectra X2, X3 are separately multiplied by coefficient vectors H1, H2, H3 determined separately in an extended projection P1.
  • the formation of the coefficient vectors is analogous to the case of only a sub-filter, wherein in K1, K2, K3 respectively the associated reference spectrum is linked to the spectrum S ⁇ of the output signal.
  • the effort is considerably increased mainly by the tripling of the projection P1. Additional storage space is required to provide the spectra of the reference signal X which is older by 1 or 2 block lengths.
  • Fig. 6 provides a more detailed block diagram of the frequency domain output FLMS algorithm and again allows comparison with Fig. 3 (Time domain output).
  • the filter adaptation consisting of smoothing of the spectral power, power normalization and coefficient renewal has remained unchanged. What is new are the FFT in the microphone channel, the difference formation YR ⁇ in the frequency domain instead of in the time domain for output formation, and finally the newly defined projection P4, which differs from the projection P1 only by the complementary time domain window.
  • Fig. 7 Shown is the FLMS algorithm with 3 sub-filters (384 sample impulse response), which provides sufficient suppression of the radio signal in the microphone channel of the speech input system.
  • the projections P1 and P4 are shown simplified. It's already out Fig. 4b known additional effort in the form of the memory D and the tripling of the projection P1 visible.
  • the filter output is now practically 3 times the smoothed spectral power is taken into account after the inverse by multiplying by the constant 6 ⁇ .
  • the filter adaptation is now carried out separately for the 3 coefficient vectors of the 3 sub-filters.
  • FIG. 9 An example Z0 for the operation of the invention according to Fig. 7 shows Fig. 9 ,
  • Microphone signal Y resulted from convolution of this noise signal with a likewise constructed 384 sample impulse response and the addition of an extremely weak speech signal. While listening to this in Fig. 9 recorded above signal y, the 10 spoken numbers are barely visible in the colored (because filtered) noise.
  • the output signal of the estimator which was transformed back into the time domain, is freed after a transient of about 1 second (12,000 samples) very effective the speech input from noise and provides an undistorted but slightly reverberated speech signal S ⁇ ( Fig. 9 below).
  • the resulting 3 * 128 sample impulse response or the associated filter transfer function can be calculated at any time. So shows Fig. 10 above is the 384 sample impulse response as it appears at the very end of the scene, that is, after the digit "0" was spoken. It is a very accurate image of the impulse response that was used to convolve with white Gaussian noise and thus to synthetically generate the signal micro.
  • White noise as the reference input signal and filtered "colored" noise as the microphone input signal are the simplest case in terms of the task of finding a replica of this filter. Since the reference signal contains all frequency components by definition, the filter adaptation succeeds fastest here.
  • the additional additive speech input in the microphone input signal - ie the actual useful signal of the speech input system - represents a disturbance for the (F) LMS algorithm, which hinders the correct adaptation of the filter coefficients. In other words, the system is only able to correctly reproduce the room acoustics of the vehicle interior (distance between radio loudspeaker and microphone) during pauses in speech thereby causing a compensation of the radio playback.
  • Fig. 9 This works very well, since the microphone input consists essentially of noise and only a very small part of speech input.
  • the reference signal radio picked up from the radio speaker terminals and the signal micro recorded from the microphone of the voice input system came from the scene Z1.
  • This microphone signal is in Fig. 11 shown above, consists of 100000 samples and thus has a sampling time of 12 kHz a time duration of about 8.3 seconds. It is fluent and relatively fast spoken language of a vehicle occupant seated in the rear right of the car while at the same time sounding at normal volume from the car radio speaker.
  • the hearing test results in a clear elaboration of the language portion or a notable especially in the short language breaks music suppression.
  • a suitable feature is used together with a threshold as an indicator for a voice input. If the characteristic falls below the threshold, this is an indication of missing speech input. In this case, as already stated above, a largely undisturbed filter adaptation can take place.
  • the filter coefficient set is used, which was stored immediately before the threshold was exceeded, ie at the end of the preceding speech break.
  • these stored coefficients H10, H20, H30 provide significantly better radio signal compensation than the current coefficients H, H2, H3, which constantly change under the disturbing influence of the voice input.
  • Fig. 8 represents an embodiment with a further improved FLMS processing with 3 sub-filters.
  • Fig. 7 existing current filter coefficient vectors H1, H2, H3, which were needed to form the continuously adopted output signal yR, there now exists an additional output signal (y-Ro) formed using stored coefficients H10, H20, H30.
  • the current coefficient sets H1, H2, H3 represent a useful compensation filter in the frequency domain only in the absence of speech input in the steady state, however, provide insufficient filter characteristics in voice input, because the adaptation process in the control loop is constantly disturbed.
  • the outputs (y-Ro) and (y-Ra) are identical. Inserting voice inputs cause the 3 switches, whereby the last located in the memories M1, M2, M3 coefficients H10, H20, H30 are no longer overwritten and remain unchanged. This state, in which the outputs (Y-Ro) and (Y-Ra) differ, is maintained until a speech break is detected again and the switches are closed.
  • a smoothing filter for example a 1st order recursive low-pass filter with the input feat, provides at its output the smoothed speech pause feature fea which, after comparison with a threshold value th, controls the coefficients transfer switches.
  • the measured 384-sample impulse response with associated magnitude transfer function is in Fig. 15 represented as current impulse response (a) or current transfer function (b).
  • an impulse response (c) and a high-quality transfer function (d) can be calculated from the stored coefficients H10, H20, H30.
  • the impulse response from the stored coefficients has the typical zero samples at the beginning, which are caused by the transit time of the direct sound from the radio loudspeaker to the speech input microphone. From the dead time of about 40 samples to be read in the example, the distance between loudspeaker and microphone can be determined.
  • the complex projection P4 (IFFT, window on the right in the time domain, FFT) can be replaced without noticeable loss of quality by a relatively simple convolution in the frequency domain, thus saving 2 FFTs.
  • IFFT IFFT, window on the right in the time domain, FFT
  • the "right-sided" 128-sample rectangle window in the time domain ( Fig. 16a ) in the ideal projection replaced by a 128 sample Hamming window ( Fig. 16b ).
  • Fig. 16b the "right-sided" 128-sample rectangle window in the time domain in the ideal projection replaced by a 128 sample Hamming window ( Fig. 16b ).
  • Fig. 17 In the case of the rectangular window, the real part of the spectrum consists of a single line (DC component), whereas the antisymmetrical part of the imaginary spectrum consists of many slowly decreasing lines with alternating lines Zeroing exists.
  • the projection P1 (IFFT - left-sided rectangular window - FFT) can be replaced by a corresponding convolution operation in the frequency domain with the conjugate complex 7-line spectrum.
  • IFFT - left-sided rectangular window - FFT IFFT - left-sided rectangular window - FFT
  • Fig. 8 cost-effective solutions can be achieved by following the LMS algorithm Fig. 8 the 3 projections P1 need not be processed simultaneously in a 256 sample input data block.
  • the 128-sample overlapping input data blocks of length 256 are numbered beginning with "1" at random Fig. 19a outlined. For example, if the input data blocks are modulo-3, the 3 sub-filter projections are not possible in parallel ( Fig.
  • the first of these scenes Z2 involves voice input of digits, with the radio loudspeaker emitting near-white noise at a relatively high volume.
  • the corresponding 100000 sample microphone signal is in Fig. 20 above, the extracted output signal in Fig. 20 shown below.
  • a clear noise exemption of the output signal compared to the microphone input is found by interception comparison.
  • the time course of the speech pause feature is together with the constant threshold th Fig. 21 pictured above and derived therefrom language pauses or the associated switch positions in Fig. 21 below.
  • Fig. 22 in to Fig.
  • the first 100000 samples of a measurement scene Z3 with POP music on the radio and fluent to fast spoken language of the person sitting on the right back are in the form of the microphone signal y in Fig. 23 recorded above.
  • the radio signal is usefully suppressed ( Fig. 23 below).
  • the POP music suppression is effectively maintained, whereby the speech intelligibility here is markedly improved over the microphone signal.
  • Fig. 24 After a long linguistic break, there is no longer a threshold underrun because of the subsequent pause-free speech input ( Fig. 24 ). For this reason, the in Fig.

Claims (10)

  1. Procédé pour la suppression de parasites d'un signal de microphone de fractions d'un signal source, qui est présent sous forme de signal de référence (x) et, après le passage d'un tronçon de transmission avec une fonction de transmission (G) a priori inconnue, se superpose dans le signal de microphone sous forme de signal parasite (r) à un signal vocal (s), par simulation adaptative du signal parasite et compensation du signal parasite réel et du signal parasite simulé dans un signal de sortie, le signal de microphone étant transformé pareillement dans la plage de fréquences, la compensation en signaux étant effectuée dans la plage de fréquences et le signal de sortie présent dans la plage de fréquences étant associé pour l'adaptation de la simulation avec le signal de référence présent dans la plage de fréquences, une fonction de filtrage adaptative d'un filtre de simulation étant appliquée au signal de référence pour la simulation du signal parasite, caractérisé en ce que l'apparition du signal vocal est détecté dans le signal du microphone et, en cas d'apparition d'un signal vocal, la fonction de filtrage réglé avant l'apparition du signal vocal est conservée pour la formation du signal de sortie.
  2. Procédé selon la revendication 1, dans lequel le spectre des signaux de sortie est transformé dans la plage de temps, le signal de temps est amené, en plaçant devant des valeurs nulles, à la longueur double, est retransformé dans la plage de fréquences et sert de base à la simulation de la fonction de transmission.
  3. Procédé selon la revendication 1, dans lequel le spectre de signaux de sortie est plié avec le spectre d'une fenêtre temporelle de Hamming et sert de base à la simulation de la fonction de transmission.
  4. Procédé selon la revendication 1, dans lequel la fonction de filtrage est prédéfinie par un vecteur de coefficients, dont les coefficients sont réglés de façon adaptative.
  5. Procédé selon la revendication 1, dans lequel, également en cas de détection du signal vocal, l'asservissement adaptatif d'une fonction de filtrage actuelle est poursuivie en supplément de la formation du signal de sortie.
  6. Procédé selon la revendication 5, dans lequel la modification du signal vocal est détectée à partir d'une modification de la fonction de filtrage actuelle.
  7. Procédé selon la revendication 6, dans lequel la modification de la fonction de filtrage actuelle est lissée dans le temps pour la détection de l'apparition d'un signal vocal.
  8. Procédé selon l'une quelconque des revendications précédentes, dans lequel la fonction de filtrage est dissociée en plusieurs fonctions de filtrage partielles à des périodes consécutives d'une réponse impulsionnelle globale de tous les filtres partiels et est appliquée à des spectres de signal de référence pour des segments de temps décalés dans le temps du signal temporel de référence segmenté.
  9. Procédé selon la revendication 8, dans lequel l'adaptation de la fonction de filtrage pour les filtres partiels est effectuée en parallèle.
  10. Procédé selon la revendication 9, dans lequel l'adaptation de la fonction de filtrage pour les filtres partiels individuels est effectuée par séquences de temps.
EP99106123A 1998-04-03 1999-04-01 Procédé pour la suppression du bruit dans un signal de microphone Expired - Lifetime EP0948237B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19814971A DE19814971A1 (de) 1998-04-03 1998-04-03 Verfahren zur Störbefreiung eines Mikrophonsignals
DE19814971 1998-04-03

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EP0948237A2 EP0948237A2 (fr) 1999-10-06
EP0948237A3 EP0948237A3 (fr) 2006-02-08
EP0948237B1 true EP0948237B1 (fr) 2008-06-11

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US (1) US6895095B1 (fr)
EP (1) EP0948237B1 (fr)
AT (1) ATE398326T1 (fr)
DE (2) DE19814971A1 (fr)

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EP0948237A2 (fr) 1999-10-06
DE59914782D1 (de) 2008-07-24
DE19814971A1 (de) 1999-10-07
EP0948237A3 (fr) 2006-02-08
ATE398326T1 (de) 2008-07-15
US6895095B1 (en) 2005-05-17

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