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Blind Source Separation by Sparse Decomposition in a Signal
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Zibulevsky, Michael; Pearlmutter, Barak A.
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The blind source separation problem is to extract the underlying source
signals from a set of linear mixtures, where the mixing matrix is unknown.
This situation is common in acoustics, radio, medical signal and image
processing, hyperspectral imaging, and other areas. We suggest a two-
stage separation process: a priori selection of a possibly overcomplete
signal dictionary (for instance, a wavelet frame or a learned dictionary) in
which the sources are assumed to be sparsely representable, followed by
unmixing the sources by exploiting the their sparse representability. We
consider the general case of more sources than mixtures, but also derive a
more efficient algorithm in the case of a nonovercomplete dictionary and
an equal numbers of sources and mixtures. Experiments with artificial
signals and musical sounds demonstrate significantly better separation
than other known techniques.
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Keyword(s):
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Computer Science; Hamilton Institute; Blind Source Separation; Sparse Decomposition; Signal Dictionary |
Publication Date:
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2001 |
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Type:
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Journal article |
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Peer-Reviewed:
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Yes |
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Institution:
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Maynooth University |
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Citation(s):
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Zibulevsky, Michael and Pearlmutter, Barak A. (2001) Blind Source Separation by Sparse Decomposition in a Signal Dictionary. Neural Computation, 13 (4). pp. 863-882. ISSN 0899-7667 |
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Publisher(s):
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Massachusetts Institute of Technology Press (MIT Press) |
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File Format(s):
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other |
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Related Link(s):
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http://mural.maynoothuniversity.ie/5485/1/BP_blind%20source.pdf |
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First Indexed:
2020-01-31 06:15:41 Last Updated:
2020-04-02 07:00:55 |
