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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 convergence performance and steady-state misalignment by considering robustness to the source position in the transmission room. We achieve this by exploiting the inherent decorrelating properties of selective-tap adaptive filtering as well as employing a variable clipping threshold for the unselected taps. Simulation results using colored noise and speech signals show an improvement over existing algorithms both in terms of convergence rate as well as steady-state normalized misalignment.

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 of the adaptive filters. The advantage of our approach as compared with existing techniques is that our algorithm, in use with the nonlinear preprocessor, can achieve a high rate of misalignment convergence without significantly degrading the quality and stereophonic image of the transmitted signals since our neural network operates on the tap-input vectors as opposed to the transmitted audio signals. We then show that we can achieve an efficient implementation for the proposed decorrelation method by considering the structure of the joint-input covariance matrix of the stereophonic signals.