A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation

In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. A common approach for separation of convolutive mixtures is the transformation to the time-frequency domain, where the convolution becomes a multiplication. This allows for the use of well-known instantaneous ICA algorithms independently in each frequency bin. However, this simplification leads to the problem of correctly aligning these single bins previously to the transformation to the time domain. Here, we propose a new criterion for solving this ambiguity. The new approach is based on the sparsity of the speech signals and yields a robust depermutation algorithm. The results will be shown on real world examples.