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    Blind Source Separation by Sparse Decomposition in a Signal Dictionary

    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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    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.

    Item Type: Article
    Keywords: Blind Source Separation; Sparse Decomposition; Signal Dictionary;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5485
    Depositing User: Barak Pearlmutter
    Date Deposited: 13 Oct 2014 15:27
    Journal or Publication Title: Neural Computation
    Publisher: Massachusetts Institute of Technology Press (MIT Press)
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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