Wavelet-Based ICA Using Maximum Likelihood Estimation and Information-Theoretic Measure for Acoustic Echo Cancellation During Double Talk Situation

Abstract

Acoustic echo cancellation (AEC) plays a prominent role in the present-day hands-free communication environment, owing to the usage of adaptive digital filter techniques. In a duplex communication scenario, there is a need for a double talk detection algorithm in the near-end speaker system which disables the update of the adaptive filter coefficients, thus hindering the process of echo cancellation and leading to a partial solution. To completely solve this problem, independent component analysis (ICA) is used to separate the far-end echo from the mixture of the near-end speech and the far-end echo signal. This paper proposes a new adaptive digital filter using maximum likelihood estimation of ICA and minimization of mutual information of ICA techniques for AEC. The advancement to this technique is made by transforming the observation into an adequate representation using wavelet decomposition. The performance of the echo cancellation is measured in terms of the echo return loss enhancement (ERLE). Higher ERLE indicates better echo cancellation. From the simulation results, it is found that the minimization of the mutual information of ICA has a higher value of ERLE than that by maximum likelihood estimation. The efficiency of the system thus increases by minimizing the processing time using wavelet ICA-based adaptive filter over the conventional adaptive filter.