Acoustic echo control. An application of very-high-order adaptive filters

Breining, Christina; Dreiscitel, P.; Hänsler, Eberhard; Mäder, Andreas; Nitsch, Bernhard; Puder, Henning; Schertler, T.; Schmidt, Gerhard; Tilp, J.

doi:10.1109/79.774933

Cited by 356 publications

(171 citation statements)

References 41 publications

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“…To update these coefficients, the normalized least mean square (NLMS) algorithm is used in this paper. This NLMS-based adaptive filter algorithm is a widely used method because of its relative simplicity and good performance [1][2][3][4][5]. Indeed, adaptation of these coefficients according to the NLMS is performed such that…”

Section: Y(n)mentioning

confidence: 99%

A second-order Volterra filter-based nonlinear clipping detector

Park¹,

Chang²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we propose a novel nonlinear clipping detector based on the second-order Volterra filter for an acoustic echo canceller (AEC). Since the performance of the conventional AEC algorithm drastically deteriorates when nonlinear clipping occurs, the adaptive filter pauses adaptation during the nonlinear clipping periods to avoid unwanted divergence. These nonlinear clipping periods are detected in our method using the magnitude spectrum of the quadratic Volterra filter in lower frequency ranges. Through extensive computer-based simulation results considering an acoustic room environment and various clipping levels, it is found that the proposed approach is effective in detecting the nonlinear clipping periods and improving AEC performance compared to the conventional AEC algorithm.

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Section: Y(n)mentioning

confidence: 99%

A second-order Volterra filter-based nonlinear clipping detector

Park¹,

Chang²

2016

Turk J Elec Eng & Comp Sci

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“…Acoustic echo cancellers have to deal with very long impulse responses because of the high reverberation times of common office environments [1]. If long impulse responses have to be taken into account, either the processed blocks have to be of sufficient length to cover the whole impulse response or a partitioned calculation has to be applied.…”

Section: Residual Echo Estimation In the Partitioned Frequency Domentioning

confidence: 99%

“…The echo signals Ψi[m, l] are attenuated by the AEC-filters Ci[m, l] which calculate an estimate of the echoesΨi[m, l] contained in the microphone path. The postfilter P [m, l] is a common enhancement technique to support the AECs which cover only a finite length of the RIRs [1]. Thus some residual echo Ξ[m, l] remains after the AECs [2], [3] and has to be canceled by the post-filter.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Partitioned Stereo Residual Echo Estimation

Goetze

Kammeyer

Kallinger

et al. 2006

2006 Fortieth Asilomar Conference on Signals, Systems and Computers

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Abstract-For convenient hands-free telecommunication systems echo cancelers with a low delay are desirable. Since such systems have to cope with long room impulse responses (RIRs) common echo cancelation techniques often suffer from slow convergence or introduce large delays. An algorithm for acoustic echo cancelation (AEC) has to compensate the influence of RIRs of several thousand sampling points. Thus for frequency domain algorithms long DFT-lengths are needed to cover the whole influence of the RIRs. Approaches in the partitioned frequency domain overcome this drawback. Our contribution describes a combined echo cancelation system in the partitioned frequency domain containing a conventional stereo AEC enhanced by a post-filter which requires a reliable residual echo estimate. This contribution focuses on minimizing the estimation bias introduced by partitioned residual echo estimation. For this purpose an optimal smoothing factor for the residual echo differential system will be derived.

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“…In hands-free telephones and desktop conferencing systems, control of echo due to acoustic coupling from the loudspeaker to the microphone is a key requirement [4]. The echo response due to the loudspeaker-room-microphone system can often be considered sparse because of the bulk delay corresponding to the direct path propagation delay from loudspeaker to microphone.…”

Section: Introductionmentioning

confidence: 99%

Adaptive algorithms for sparse echo cancellation

2006

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The cancellation of echoes is a vital component of telephony networks. In some cases the echo response that must be identified by the echo canceller is sparse, as for example when telephony traffic is routed over networks with unknown delay such as packet-switched networks. The sparse nature of such a response causes standard adaptive algorithms including normalized LMS to perform poorly. This paper begins by providing a review of techniques that aim to give improved echo cancellation performance when the echo response is sparse. In addition, adaptive filters can also be designed to exploit sparseness in the input signal by using partial update procedures. This concept is discussed and the MMax procedure is reviewed. We proceed to present a new high performance sparse adaptive algorithm and provide comparative echo cancellation results to show the relative performance of the existing and new algorithms. Finally, an efficient low cost implementation of our new algorithm using partial update adaptation is presented and evaluated. This algorithm exploits both sparseness of the echo response and also sparseness of the input signal in order to achieve high performance without high computational cost. r

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Acoustic echo control. An application of very-high-order adaptive filters

Cited by 356 publications

References 41 publications

A second-order Volterra filter-based nonlinear clipping detector

A second-order Volterra filter-based nonlinear clipping detector

Enhanced Partitioned Stereo Residual Echo Estimation

Adaptive algorithms for sparse echo cancellation

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