A Perspective on Stereophonic Acoustic Echo Cancellation

Benesty, Jacob; Paleologu, Constantin; Gänsler, Tomas; Ciochină, Silviu

doi:10.1007/978-3-642-22574-1

Cited by 49 publications

(46 citation statements)

References 9 publications

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“…Two source signals are used: a white Gaussian signal and a speech sequence. The input signals are preprocessed with positive and negative half-wave rectifiers, using a distortion parameter α r = 0.3 [1].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For P = 1, the WL-GKF has striking resemblances with the WL-RLS algorithm [1], [6]. However, the two algorithms are very much different and do not behave the same way in practice.…”

Section: General Kalman Filter For Wl Saecmentioning

confidence: 90%

“…However, there are other aspects that should be taken into account in practice (e.g., nonlinear processing of the input signals, doubletalk detection, echo/noise suppression, implementation issues, etc. ), which make more convenient to handle only one adaptive filter instead of four such systems [1].…”

Section: Saec With the Wl Modelmentioning

confidence: 99%

“…In our recent work [1], [6], we proposed a different approach for the SAEC problem, by using the widely linear (WL) model [7]. In fact, the classical two-input/two-output system with real random variables was recast as a single-input/singleoutput system with complex random variables.…”

Section: Introductionmentioning

confidence: 99%

“…The stereophonic acoustic echo cancellation (SAEC) problem is very challenging from a system identification perspective [1]. Usually, an SAEC system consists of four adaptive filters aiming at identifying four echo paths from two loudspeakers to two microphones.…”

Section: Introductionmentioning

confidence: 99%

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A Kalman filter for stereophonic acoustic echo cancellation

Paleologu

Benesty

Grant

et al. 2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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The stereophonic acoustic echo cancellation (SAEC) scheme was recently recast by using the widely linear (WL) model, i.e., as a single-input/single-output system with complex random variables. In this paper, we present a Kalman filter with individual control factors in the context of the WL model for SAEC. As a specific feature, this algorithm uses a different level of uncertainty for each coefficient of the filter. Simulation results indicate that the proposed algorithm outperforms the recursive least-squares (RLS) algorithm, which is usually considered as the benchmark for SAEC.

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Section: Simulation Resultsmentioning

confidence: 99%

“…For P = 1, the WL-GKF has striking resemblances with the WL-RLS algorithm [1], [6]. However, the two algorithms are very much different and do not behave the same way in practice.…”

Section: General Kalman Filter For Wl Saecmentioning

confidence: 90%

Section: Saec With the Wl Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Kalman filter for stereophonic acoustic echo cancellation

Paleologu

Benesty

Grant

et al. 2014

2014 48th Asilomar Conference on Signals, Systems and Computers

Self Cite

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New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

Djendi

2014

Adaptive Control & Signal

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This paper addresses the field of stereophonic acoustic echo cancelation (SAEC) by adaptive filtering algorithms. Recently, simplified versions of the fast transversal filter (SFTF)-type algorithm has been proposed. In this paper, we propose two major contributions. In the first contribution, we propose two new FTF-type algorithms with low complexity and good convergence speed characteristics. These two proposed algorithms are mainly on the basis of a forward prediction scheme to estimate the so called dual Kalman gain, which is inherent in the filtering part update. This computation complexity is achieved by the introduction of new relations for the computation of the likelihood variables that are simple and lead to further simplifications on the prediction part of the two proposed algorithms. In the second contribution, we propose to adapt then apply these four new SFTF-type algorithms, (the two proposed algorithms in this paper and their original versions) in the SAEC applications. A fair comparison of the proposed algorithms with the original SFTF and the normalized least mean square algorithms, in mono and SAEC applications, is presented. Copyright 274 M. DJENDI Recently, a different approach for the SAEC problems (inter-channel coherence, adaptive filter lengths, etc.) was proposed in [18,19] by using the widely linear model, which is assigned to the processing of noncircular complex valued signals. A new hybrid solution to the same problem that uses frequency shifts to improve the performance of the state-of-art solution based on addition of half-wave rectified signals is presented in [20]. The authors in [21] proposed a windowing frequency domain adaptive filter, and an up-sampling block transforms preprocessing, to solve these constraints. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in SAEC can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain up-sampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Another very recent and novel approach to SAEC is proposed in [22] and based on a combination of psychoacoustic effects. The principle of the proposed structure is to use an approach based on the 'missing-fundamental' phenomenon at low frequencies (below 500 Hz) and a modified phase modulation approach at higher frequencies.A basic scheme for SAEC is given in Figure 1, where we illustrate the concept with a transmission room on the left and a receiving room on the right [16,17,23,24]. In Figure 1, when we generate a signal in the transmission room, the two microphones mic 1 and mic 2 receive two sorts of signals: (i) the direct source signal modified by the path g 1 (then received by mic 1 / and g 2 (then received by mic 2 /, respectively, and (ii) the existing diffuse noises n 1 and n 2 in the transmission room and received ...

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