MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and -Norm Minimization

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ID: 56048
2007
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Abstract

We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP) approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the -norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP) approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.

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walter2007mapbasedeurasip Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Walter, Kellermann;Stefan, Winter;Hiroshi, Sawada;Shoji, Makino;
Journal eurasip journal on advances in signal processing
Year 2007
DOI
DOI not found
URL
http://asp.eurasipjournals.com/content/2007/024717
Keywords
Medicine Engineering (General). Civil engineering (General) Biotechnology Computer applications to medicine. Medical informatics Science neurosciences. biological psychiatry. neuropsychiatry telecommunication electronics mathematics

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