High resolution sub-band decomposition underdetermined blind signal separation using virtual sensor based ICA method for low latency applications

Abstract

Underdetermined blind source separation (UBSS) objective is to recover the source signals from a number of mixtures without any information about the mixing system. Technologies such as teleconferencing, hearing aids and hands-free telephony require real time processing, as long delays are considered intolerable in interactive two-way communication. This is a major challenge in BSS, as algorithms generally require significant amounts of data (several seconds of data or more) to generate sufficient statistics for separation. In this paper, we propose a hybrid algorithm which combines sub-band decomposition and well-known Independent Component Analysis (ICA) based algorithm, Extended-Infomax. We first employ sub-band decomposition algorithm in sparse time domain to compensate low data efficiency of short time block lengths and estimate the mixing matrix. Subsequently, the proposed virtual sensor based underdetermined Extended-Infomax source model is used to estimate the source signals. As the separation evaluation results are so sensitive to the mixing matrix estimation outcome, achieving nearly optimum results in the first phase of the proposed algorithm significantly improves the results of source separation performance. A weighted version of k-plane clustering algorithm is derived to obtain the mixing coefficients. Experimental evaluations reveal the effectiveness of our proposed method over the state-of-the-art techniques.