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Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Zibulevsky, Michael; Pearlmutter, Barak A.
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.
Keyword(s): Computer Science; Hamilton Institute; Blind Source Separation; Sparse Decomposition; Signal Dictionary
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): 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
Publisher(s): Massachusetts Institute of Technology Press (MIT Press)
File Format(s): other
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First Indexed: 2020-01-31 06:15:41 Last Updated: 2020-04-02 07:00:55