UNDERDETERMINED SPARSE BLIND SOURCE SEPARATION WITH DELAYS

In this paper, we address the problem of under-determined blind source separation (BSS), mainly for speech signals, in an anechoic environment. Our approach is based on exploiting the sparsity of Gabor expansions of speech signals. For parameter estimation, we adopt the clustering approach of DUET [19]. However, unlike in the case of DUET where only two mixtures are used, we use all available mixtures to get more precise estimates. For source extraction, we propose two methods, both of which are based on constrained optimization. Our first method uses a constrained l (0 < q ≤ 1) approach, and our second method uses a constrained “modified” l minimization approach. In both cases, our algorithms use all available mixtures, and are suited to the anechoic mixing scenario. Experiments indicate that the performances of the proposed algorithms are superior compared to DUET in many different settings.