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ICA
2007
Springer

Two Improved Sparse Decomposition Methods for Blind Source Separation

9 years 1 months ago
Two Improved Sparse Decomposition Methods for Blind Source Separation
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.
B. Vikrham Gowreesunker, Ahmed H. Tewfik
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ICA
Authors B. Vikrham Gowreesunker, Ahmed H. Tewfik
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