Two improved sparse decomposition methods for blind source separation

Authors:
B. Vikrham Gowreesunker;Ahmed H. Tewfik
Affiliations:
Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN;Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

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Abstract

In underdetermined blind source separation problems, it is common practice to exploit the underlying sparsity of the sources for demixing. In this work, we propose two sparse decomposition algorithms for the separation of linear instantaneous speech mixtures. We also show how a properly chosen dictionary can improve the performance of such algorithms by improving the sparsity of the underlying sources. The first algorithm proposes the use of a single channel Bounded Error Subset Selection (BESS) method for robustly estimating the mixing matrix. The second algorithm is a decomposition method that performs a constrained decomposition of the mixtures over a stereo dictionary.