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.
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© 2007 Springer-Verlag Berlin Heidelberg
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Gowreesunker, B.V., Tewfik, A.H. (2007). Two Improved Sparse Decomposition Methods for Blind Source Separation. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_46
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DOI: https://doi.org/10.1007/978-3-540-74494-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
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