Hierarchical clustering applied to overcomplete BSS for convolutive mixtures

In this paper we address the problem of overcomplete BSS for convolutive mixtures following a two-step approach. In the first step the mixing matrix is estimated, which is then used to separate the signals in the second step. For estimating the mixing matrix we propose an algorithm based on hierarchical clustering, assuming that the source signals are sufficiently sparse. It has the advantage of working directly on the complex valued sample data in the frequency-domain. It also shows better convergence than algorithms based on selforganizing maps. The results are improved by reducing the variance of direction of arrival. Experiments show accurate estimations of the mixing matrix and very low musical tone noise even in reverberant environment.