ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 128

Two improved sparse decomposition methods for Blind Source Separation

Author(s): B. Vikrham Gowreesunker, Ahmed H. Tewfik


In underdetermined Blind Source Separation problems, it is common practice to exploit the underlying sparsity of the sources for demixing. In this work, we show how trained dictionary can improve sparsity of the underlying sources. We also propose two sparse decomposition algorithms for the separation of linear instantaneous speech mixtures. The first algorithm uses a single channel Bounded Error Subset Selection (BESS) method to estimate the mixing matrix. The second sparse decomposition method performs a constraint decomposition of the mixtures over a stereo dictionary.

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