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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Gowreesunker, B. V., & Tewfik, A. H. (2007). Two improved sparse decomposition methods for blind source separation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 365–372). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_46
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