Neural network based adaptive echo cancellation for stereophonic teleconferencing application

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 the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-input correlation matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.