A Linear Neural Network-Based Approach to Stereophonic Acoustic Echo Cancellation

Abstract

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.