EP3065417B1 - Procede de suppression d'un bruit parasite dans un systeme acoustique - Google Patents

Procede de suppression d'un bruit parasite dans un systeme acoustique Download PDF

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Publication number
EP3065417B1
EP3065417B1 EP16151092.0A EP16151092A EP3065417B1 EP 3065417 B1 EP3065417 B1 EP 3065417B1 EP 16151092 A EP16151092 A EP 16151092A EP 3065417 B1 EP3065417 B1 EP 3065417B1
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Prior art keywords
signal
filter
microphone
output signal
input
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German (de)
English (en)
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EP3065417A1 (fr
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Tobias Daniel Rosenkranz
Tobias Wurzbacher
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Sivantos Pte Ltd
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Sivantos Pte Ltd
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/002—Devices for damping, suppressing, obstructing or conducting sound in acoustic devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R25/00—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception
    • H04R25/45—Prevention of acoustic reaction, i.e. acoustic oscillatory feedback
    • H04R25/453—Prevention of acoustic reaction, i.e. acoustic oscillatory feedback electronically
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3026—Feedback
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters
    • 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
    • H04R3/02—Circuits for transducers, loudspeakers or microphones for preventing acoustic reaction, i.e. acoustic oscillatory feedback

Definitions

  • the invention relates to a method for suppressing a noise in an acoustic system, wherein the acoustic system comprises at least one microphone and at least one speaker, and wherein the at least one microphone generates an input signal, wherein the at least one loudspeaker generates an acoustic signal, which partially the at least one microphone feeds back.
  • noise may occur due to feedback.
  • Acoustic feedback may result from the fact that the acoustic signal generated by the speaker is partially perceived by the microphone, and thereby re-entry into the acoustic system.
  • the input signal generated by the microphone is amplified in the acoustic system, so that within the closed loop, which is formed by the speaker, the acoustic signal generated by this, the microphone, and the signal processing within the acoustic system, a signal component by the feedback continues is amplified to a whistling noise when the gain in the signal processing within the acoustic system exceeds a certain limit.
  • k is the discrete time index
  • x is the input to the feedback cancellation system
  • ⁇ is the step size over which the speed of adaptation or convergence is controlled, and * denotes the complex conjugation.
  • the input signal m is often first digitized with a comparatively high sampling rate and thereby converted into time-discrete sampled values. Subsequently, in each case a multiplicity, for example 128, of consecutive samples is combined to form a so-called frame.
  • a spectral analysis of the input signal can now be carried out on the basis of the samples forming the frame by means of Fourier transformation.
  • the window to be considered is shifted in the direction of the time axis by a few sample values, for example 32, so that the windows of the respective samples to be taken into account for one frame overlap partially for adjacent frames.
  • the time index can in this case be understood as a frame index, whereby the adaptive filter can also be used in the frequency domain.
  • the filter coefficients h are vectors whose entries each correspond to a spectral subband.
  • the application is not limited to this case. Details can be found for example in S. Haykin, "Adaptive Filter Theory” (Englewood Cliffs, NJ: Prentice-Hall, 1996 ) or T. v. Waterschoot & M. Moonen, "Fifty years of acoustic feedback control: state of the art and future challenges "(Proc. IEEE, Vol. 99, No. 2, Feb. 2011, pages 288-327 ).
  • correlated input signals such as may be generated by the recording of music or spoken language
  • an adaptive filter which may result in at least partial cancellation of a target signal.
  • This can produce noticeable signal artifacts in the output signal, resulting in a significant deterioration of the sound quality.
  • the whistling noise generated by acoustic feedback also has a high correlation in the signals concerned, particularly when there is a correlated target signal which is picked up and fed back through a loudspeaker after playback. If an adaptive filter is to be used to suppress the interference noise generated thereby, signal components of the target signal can also be at least partially extinguished in the suppression of the interference signal of the feedback, which has a negative effect on the sound quality of the output signal.
  • the EP 2 736 271 A1 discloses a system for estimating a feedback noise signal in which an output signal is shifted in frequency. The frequency-shifted output signal is then supplied as an input to an adaptive filter for rejecting a noise signal and to an algorithm for adjusting the filter parameters of the adaptive filter.
  • the invention is therefore based on the object of mentioning a method for suppressing an acoustic noise caused by noise, which allows the use of an adaptive filter, and at the same time has the highest possible sound quality in the output signal.
  • the acoustic system comprises at least one microphone and at least one speaker, wherein the at least one microphone generates an input signal and wherein the at least one loudspeaker generates an acoustic signal, which partially feeds back to the at least one microphone, wherein along a main signal path in response to the input signal, a first intermediate signal and from the first intermediate signal by a frequency distortion an output signal is formed, wherein the output signal is coupled out of the main signal path in a signal feedback path, wherein in the signal Feedback path from the output signal by decorrelation a second intermediate signal is formed, which is used as an input to an adaptive filter, which generates a compensation signal, and wherein the Kompensat ion signal is supplied to the input signal for compensation, wherein from the input signal and / or from the compensated input signal, a third intermediate signal is formed, which is used as an input to the adaptive filter, and wherein the output signal is supplied to the at
  • the output signal can also be used as a further input variable for the adaptive filter, wherein the second intermediate signal and the third intermediate signal are used in the adaptive filter for determining filter coefficients, by means of which the output signal is filtered and the compensation signal is thereby generated.
  • the invention is based on the following considerations: A reduction in the step size p of an adaptive filter used would result in the filter diverging much more slowly in the case of a correlated input signal, so that undesired artifacts in the output signal could be reduced or become inaudible.
  • the reduction of the step size could, for example, always take place when a correlated or tonal input signal is registered.
  • a disadvantage of such a procedure is that any change in the acoustic feedback path while the correlated signal is being registered can not be tracked fast enough to avoid feedback noise, because of the reduced step size p limitations on the adaptability of the filter be placed.
  • the step size is therefore always to be seen as a trade-off between sound quality and the ability to respond to changes in the acoustic feedback path.
  • Another way to solve the problems of an adaptive filter for a strongly correlated input signal is a possible decorrelation of the input signal (so-called "pre-whitening"). Since only correlated input signals cause matching problems in the adaptive filter, such decorrelation could first solve the problem.
  • Such decorrelation is often implemented by a linear predictor. For a correlated input signal, a prediction is made for one or more future samples of the signal as a function of past observed samples of the signal. This prediction then becomes the actual input signal subtracted. The result of this subtraction is called the prediction error signal ("residual signal"). For example, a sinusoidal signal is completely deterministic and therefore perfectly predictable. In this case, for a corresponding prediction order, the residual signal would be zero.
  • s (k) represents the sample of the input signal for the prediction at time k
  • a (i) denotes the filter coefficient of decorrelation
  • P the order of prediction.
  • the prediction error signal thus generated is generally of complex value.
  • Interference caused by feedback also has significantly correlated signal components. If a decorrelation is applied to such a signal, the signal strength of the resulting prediction error signal is very low. For reuse in an adaptive filter, this would mean that the adaptive filter is not excited at the frequency of the noise generated by the feedback. Thus, the filter can not adapt to the acoustic feedback path at this frequency, whereby the noise remains until the acoustic feedback path changes.
  • the autocorrelation values are time-dependent, and therefore preferred to be repeatedly determined.
  • most non-stationary signals can be considered nearly stationary within a time window of a certain duration. The length of this time window depends on the degree to which the signal is not stationary.
  • the adaptation speed of a filter or estimator which calculates the autocorrelation values of an input signal, plays an important role in this case: the faster the estimator, the better it is not possible to track stationary signals, which improves the decorrelation of an input signal.
  • the adaptation speed and thus the ability to decorrelate nonstationary signals is regulated by the step size.
  • a correlated target signal for the adaptive filter for canceling out a feedback-related noise is preferably decorrelated beforehand, but that a decorrelation of the adaptive filter at the frequencies of the feedback-induced noise no longer stimulates, could now be avoided, that in a first step, such a noise is detected, and in response to such detection in a second step in this case, the decorrelation omitted.
  • this has several practical disadvantages: First, such a detection in practice is always faulty. In particular, if the acoustical feedback stimulates a plurality of closely spaced frequencies, these may not be sufficiently suppressed due to insufficient spectral resolution in the detection.
  • Another possibility could be to determine the filter coefficients for the decorrelation in another acoustic system, and to transmit these filter coefficients continuously between the participating acoustic systems for adaptation. This possibility would be given in particular in a binaural hearing aid system.
  • the above idea would be based on the assumption that the sound signals from the environment recorded by the respective acoustic systems have a high degree of similarity, but that noise caused by feedback in a single system affects only the individual acoustic system. Since feedback noise at a given frequency will most likely only occur in one acoustic system, the filter coefficients for decorrelation determined in another acoustic system may be considered a good estimate for the decorrelation of a target signal in the feedback acoustic system are used.
  • the presence of a further acoustic system is first required, which is often not the case.
  • the filter coefficients occur so that they are no longer up-to-date when receiving in the other acoustic system, or due to the spatial arrangement of the acoustic systems involved, the respective filter coefficients are not a sufficiently good estimate for the other system. This may, for example, in a binaural hearing aid system caused by the head of the user shading effects occur.
  • the invention proposes to first subject an output signal of the acoustic system, which is to be fed into a signal feedback path, to frequency distortion, and then to decorrelate it.
  • a time-dependent frequency distortion can be used here.
  • Noise caused by feedback usually has a nearly perfectly sinusoidal signal. Due to the frequency distortion, this form is lost. If, for example, a time-dependent frequency shift is selected for the frequency distortion, the signals of the noise follow this frequency shift.
  • the autocorrelation values of frequency-distorted signals decrease as the time interval increases, so that the window of time during which the feedback-induced noise can be considered stationary is shortened.
  • a decorrelator such that it does not adapt to the interference signal of the feedback.
  • the time window in which signals can be considered stationary is preferably to be chosen so that the interference signal of the feedback is not considered stationary by the frequency distortion, the actually non-stationary signal components of a target signal already.
  • the decorrelation is not adapted to the interference signal, but only to the signal components of the target signal, which are decorrelated.
  • the decorrelated signal the non-stationary correlated signal components are removed, as they occur in the recording of spoken speech, but not caused by the feedback Signal components.
  • the decorrelated signal is now supplied as an intermediate signal to the adaptive filter, which can generate a compensation signal based on the feedback caused by feedback, which is fed back to the main signal path for the suppression of the noise.
  • the input signal is favorably time-discretized, whereby a "least mean square” algorithm (LMS) is used as the adaptive filter.
  • LMS least mean square algorithm
  • the output signal is preferably used as the reference signal, and the error signal of the LMS filter is formed by the difference between the input signal and the compensation signal.
  • the specified method is particularly advantageous in the use of an LMS algorithm in the adaptive filter, since the frequency distortion of the output signal solves the divergence problems which occur when using an LMS algorithm for the adaptive filtering of feedback-related interference signals.
  • the step size in the LMS algorithm is normalized via the second intermediate signal.
  • This procedure is also called “normalized least mean square” (NLMS).
  • NLMS normalized least mean square
  • Such normalization improves the convergence properties of the algorithm.
  • the optimal filter coefficients are generally given by the solution of the filter equation by means of a Wiener filter. However, due to the static properties and the limited conversion time, this can usually not be used, which is why estimates are used for the filter coefficients given by the Wiener filter, the estimates converging ideally against the Wiener solution.
  • the frequency distortion to form the output signal from the first intermediate signal is achieved by a frequency shift.
  • a frequency shift is used.
  • This provides the ability to tune the decorrelator's adaptation speed to the frequency offset, and thus effectively exclude the frequency-shifted signal components of the acoustic feedback noise from the decorrelation.
  • frequency distortion may also be by phase modification, frequency transposition, or non-linear transformation.
  • the adaptation speed of the decorrelator is preferably to be tuned to the respective degree of frequency distortion.
  • the output signal for decoding the second intermediate signal is decorrelated by means of a linear prediction filter.
  • the filter coefficients of the linear prediction filter are preferably to be determined by means of a Levinson-Durbin recursion or by means of an LMS or NLMS algorithm.
  • the advantage of a linear prediction filter is that only linear systems of equations have to be solved, which limits the numerical complexity for the respective filter problem.
  • the input signal or the compensated input signal can also be decorrelated by means of a linear prediction filter, and used to form the third intermediate signal, which is supplied as an input variable to the adaptive filter.
  • time-dependent autocorrelation values of the output signal and / or of a signal based on the input signal are preferred for the filter coefficients of the linear prediction filter Error signal used.
  • the autocorrelation values can be used for a Levinson-Durbin algorithm. Taking into account the time dependence of the autocorrelation values allows the decorrelation to be adjusted to the degree of frequency distortion via the appropriate choice of a corresponding time window, after which the autocorrelation values are again determined.
  • the filter coefficients of, in particular, each linear prediction filter are adapted as a function of the decorrelation strength of the frequency distortion.
  • the time window in which signals can be regarded as stationary depends on the decorrelation strength of the frequency distortion.
  • this can be done, for example, via a repeated adaptation of the autocorrelation values in the mentioned time intervals, from which the filter coefficients are to be determined again.
  • the step size can instead be adjusted accordingly in the time intervals specified.
  • the described functional dependence of the time intervals or of the stationary time window can influence which signal components are still perceived as stationary by the decorrelator, so that the signal components of the interference signal affected by the frequency distortion are not decorrelated.
  • a decorrelator which has too short a "stationary time window", could also perceive signal components of a frequency-distorted originally monofrequency signal as stationary and therefore decorrelate it. This is circumvented by adapting the rate of adaptation of the decorrelation to the degree of frequency distortion, in particular to that of its own decorrelation strength. If, for example, a time-dependent frequency shift is selected, then this is preferably carried out more quickly than signals are considered to be stationary in the time window for the decorrelation.
  • the filter coefficients of the, in particular each linear prediction filter are adapted in dependence on a transfer function of a model of the acoustic system, which comprises the at least one microphone and at least one speaker reproducing the corrected output signal.
  • the time intervals for the adaptation of the filter coefficients may additionally depend on the decorrelation strength of the frequency distortion.
  • the transfer function may hereby contain the specific characteristics of the acoustic system, e.g. Gain values in individual sub-bands. In such a model, it is also possible, at least implicitly via coefficients of the transfer function, to enter into the probability that a feedback causes noise at a certain frequency.
  • the decorrelation adaptation rate may be reduced to ensure that the frequency-distorted components of the original monofrequency noise are not considered stationary and decorrelated. If feedback is unlikely, the time window for the decorrelator adaptation can be shortened so that tonal signal components, e.g. generated by voice recording, are quickly detected, and decorrelated.
  • the invention further provides an acoustic system comprising at least one microphone for generating an input signal, at least one loudspeaker for reproducing an output signal, and a control unit which is adapted to generate a noise by feedback of the output signal reproduced via the at least one loudspeaker is caused in the input signal generated by the at least one microphone, suppress by the above-described method.
  • the acoustic system is here as a hearing aid, and advantageous as a Hearing aid formed.
  • FIG. 1 3 shows a schematic block diagram of the sequence of a method 1 for suppressing a noise g in an acoustic system 2.
  • the acoustic system 2 which is given here by a hearing device 3, for example a hearing aid device, comprises a microphone 4 and a loudspeaker 6.
  • the microphone signal m recorded by the microphone 4 is fed to a signal processing unit 10 in a main signal path 8 where it amplifies, among other things becomes.
  • an output signal xs is output to the microphone 4, which generates an acoustic signal p from the output signal xs.
  • a part of the acoustic signal p generated by the loudspeaker 6 is again recorded by the microphone 4 as feedback fb, and thus finds its way into the microphone signal m.
  • the feedback fb signal components of the acoustic signal p in the microphone signal m are fed again to the signal processing unit 10 and further amplified there.
  • the repeated amplification, playback and recording in a closed process produces noise g in the form of almost monofrequente whistling sounds.
  • the signal feedback path 16 is provided.
  • the output signal x s is coupled out of the main signal path 8 and fed to a decorrelator 18.
  • the decorrelator 18 is formed by a linear prediction filter 20.
  • the signal processing unit 10 outputs a first intermediate signal x, which is converted by a frequency distortion 22 in the output signal xs.
  • the frequency distortion 22, which is achieved in the present case by a frequency shift 23, has the consequence that the linear prediction filter 20 does not decorrelate the signal components corresponding to the noise g, but only signal components of a target signal.
  • a second intermediate signal xw is output as an input to an adaptive filter 24.
  • the adaptive filter 24 generates from the output signal xs a compensation signal c, which is subtracted from the microphone signal m to compensate for the noise g. As a result, the signal feedback path 16 is closed.
  • the adaptive filter 24 is supplied with an additional intermediate signal ew as an input signal.
  • This third intermediate signal ew is formed from the error signal e, which results from the microphone signal m compensated for the compensation signal, c.
  • the error signal e is now likewise decorrelated by a linear prediction filter 26, and the decorrelated error signal ew is supplied as a second input variable to the adaptive filter 24.
  • the coefficients h are calculated in a filter block 28 of the adaptive filter 24, from which a signal block 30 of the adaptive filter together with the output signal xs generates the compensation signal c.
  • the frequency shift 23 ensures that the linear prediction filter 20 does not decorrelate any signal components associated with the noise g, whereby the adaptive filter 24 would no longer compensate for these with the compensation signal c.
  • the length of the stationary time window T of the linear prediction filters 20, 26, and thus their adaptation speed, is thereby controlled as a function of the frequency shift 23.
  • a control unit 32 in the hearing aid 3 performs all specified method steps.
  • FIG. 2 is in a block diagram a slight modification of the in FIG. 1 shown method 1 shown.
  • the decorrelated error signal ew which is supplied as an input to the adaptive filter, is formed from an input signal mw decorrelated in the linear prediction filter 26 and a decorrelated compensation signal cw.
  • the decorrelated compensation signal cw is formed in the filter block 28 of the adaptive filter from the error signal ew decorrelated in the linear prediction filter 26 and the second intermediate signal xw, which is given by the output signal xs decorrelated in the linear prediction filter 20.
  • the length of the stationary time window T of the linear prediction filters 20, 26, and thus their adaptation speed is hereby determined by an adaptation control 34, in which the degree df of the frequency shift 23, the gain n of the signal processing unit 10 in individual sub-bands, and a non find more detailed transfer function of the acoustic system 2 input and used to determine the time window T.
  • an adaptation control 34 in which the degree df of the frequency shift 23, the gain n of the signal processing unit 10 in individual sub-bands, and a non find more detailed transfer function of the acoustic system 2 input and used to determine the time window T.
  • a model of the acoustic feedback path fb determined by the filter coefficients h can also be used, so that the adaptation speed of the decorrelation in the linear prediction filters 20, 26 is also determined as a function of the feedback estimated by this model.
  • the use of such an adjustment control 34 is thereby not on the in FIG. 2 illustrated form of the signal feedback path 16 is limited, but can in principle in various embodiments, in particular

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Claims (8)

  1. Procédé (1) pour inhiber un bruit parasite (g) dans un système acoustique (2),
    le système acoustique (2) comportant au moins un microphone (4) et au moins un haut-parleur (6),
    l'au moins un microphone (4) générant un signal d'entrée (m) et l'au moins un haut-parleur (6) générant un signal acoustique (p), lequel est partiellement rebouclé sur l'au moins un microphone (4),
    le long d'un trajet de signal principal (8), un premier signal intermédiaire (x) étant formé en fonction du signal d'entrée (m) et un signal de sortie (xs) à partir du premier signal intermédiaire (x) par une distorsion en fréquence (22),
    le signal de sortie (xs) étant découplé hors du trajet de signal principal (8) dans un trajet de rebouclage de signal (16),
    un deuxième signal intermédiaire (xw) étant formé dans le trajet de rebouclage de signal (16) à partir du signal de sortie (xs) par une décorrélation (18) au moyen d'un filtre à prédiction linéaire (20), les coefficients de filtrage du filtre à prédiction linéaire (20) étant adaptés en fonction de l'intensité de décorrélation de la distorsion en fréquence (22), le deuxième signal intermédiaire (xw) étant utilisé comme grandeur d'entrée pour un filtre adaptatif (24), lequel génère un signal de compensation (c), et le signal de compensation (c) étant acheminé au signal d'entrée (m) en vue de la compensation,
    un troisième signal intermédiaire (ew) étant formé à partir du signal d'entrée (m) et/ou à partir du signal d'entrée compensé (e), lequel est utilisé comme grandeur d'entrée pour le filtre adaptatif, et
    le signal de sortie (xw) étant acheminé à l'au moins un haut-parleur (4) pour restitution.
  2. Procédé (1) selon la revendication 1, le signal d'entrée (m) étant discrétisé dans le temps et le filtre adaptatif utilisé étant un algorithme des moindres carrés moyens (LMS).
  3. Procédé (1) selon la revendication 2, la taille de pas de l'algorithme LMS étant normalisée sur le deuxième signal intermédiaire (xw).
  4. Procédé (1) selon l'une des revendications précédentes, la distorsion en fréquence (22) en vue de former le signal de sortie (xs) étant atteinte à partir du premier signal intermédiaire (x) par un décalage en fréquence (23).
  5. Procédé (1) selon l'une des revendications précédentes, des valeurs d'autocorrélation du signal de sortie (xs) dépendantes du temps et/ou un signal de défaut (e) basé sur le signal d'entrée (m) étant utilisés pour les coefficients de filtrage du filtre à prédiction linéaire (20).
  6. Procédé (1) selon l'une des revendications précédentes, les coefficients de filtrage du filtre à prédiction linéaire (20) étant adaptés en fonction d'une fonction de transfert d'un modèle du système acoustique (2), lequel comporte l'au moins un microphone (4) et au moins un haut-parleur (6) qui restitue le signal de sortie corrigé (xs).
  7. Système acoustique (2) comportant au moins un microphone (4) destiné à générer un signal d'entrée (m), au moins un haut-parleur (6) destiné à restituer un signal de sortie (xs) et une unité de commande (32), laquelle est conçue pour inhiber un bruit parasite (g) par rebouclage du signal de sortie (xs) restitué par l'au moins un haut-parleur (6) dans le signal d'entrée (m) généré par l'au moins un microphone (4) par un procédé (1) selon l'une des revendications précédentes.
  8. Système acoustique (2) selon la revendication 7, lequel est réalisé sous la forme d'un appareil auditif (3), notamment sous la forme d'un appareil d'aide auditive.
EP16151092.0A 2015-03-05 2016-01-13 Procede de suppression d'un bruit parasite dans un systeme acoustique Active EP3065417B1 (fr)

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DE102015204010.0A DE102015204010B4 (de) 2015-03-05 2015-03-05 Verfahren zur Unterdrückung eines Störgeräusches in einem akustischen System

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US11468873B2 (en) * 2017-09-29 2022-10-11 Cirrus Logic, Inc. Gradual reset of filter coefficients in an adaptive noise cancellation system
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EP4029018B1 (fr) * 2019-09-11 2023-07-26 DTS, Inc. Amélioration de l'intelligibilité de la voix sensible au contexte

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EP3065417A1 (fr) 2016-09-07
DE102015204010A1 (de) 2016-09-08
US20160260423A1 (en) 2016-09-08
DK3065417T3 (da) 2019-03-04
US9824675B2 (en) 2017-11-21
DE102015204010B4 (de) 2016-12-15

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