Mehdi Bekrani

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The use of partial-updating algorithm for reducing interchannel coherence in stereophonic acoustic echo cancellation has been proposed recently. In this work, we show that this algorithm suffers from the lack of robustness against source positions in the transmission room. To address this, we present an insight into this problem and propose a center(More)
Stereophonic acoustic echo cancellation remains one of the challenging areas for tele/video-conferencing applications. However, the existence of high interchannel coherence between the two input signals for such systems leads to considerable degradation in misalignment convergence of the adaptive filters. We propose a new algorithm for improving the(More)
In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-input correlation matrix with an emphasis on reducing its high cross-correlation components. We derive an estimate of the inverse joint-input(More)
Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of(More)
Stereophonic acoustic echo cancellers (SAEC) have extended application in stereo teleconferencing systems. They have problems more severe than monophonic echo cancellers. The most important problem is the misalignment. This problem causes a severe divergence of filters in the event of abrupt changes of the transmission room acoustic paths. In this paper, we(More)
We propose a new adaptive filtering algorithm for stereophonic acoustic echo cancellation. This algorithm uses a linear single-layer feedforward neural network to efficiently decorrelate the tap-input vectors. It achieves an improvement in the misalignment convergence by means of applying the resulted decorrelated tap-input vectors to the coefficient update(More)
The three-level clipped input least-mean-square (CLMS) adaptive algorithm is known to have low complexity that is suitable for the identification of long finite impulse response of unknown systems. In this paper we analyze the performance of CLMS which allows one to gain insights into its convergence property and the amount of steady-state misalignment(More)
In this paper, we present a new partial update NLMS adaptive filtering algorithm for improving the performance of stereophonic acoustic echo cancellers. The proposed partial update approach brings about a low interchannel coherence, independent of the location of the source in the transmission section which in turn increases the robustness to source(More)
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