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    Blind source separation via multinode sparse representation

    Zibulevsky, Michael and Kisilev, Pavel and Zeevi, Yehoshua Y. and Pearlmutter, Barak A. (2002) Blind source separation via multinode sparse representation. Advances in Neural Information Processing Systems 14. pp. 1049-1056. ISSN 9780262042086

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    We consider a problem of blind source separation from a set of instan taneous linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appro priate representation according to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multiscale transforms, such as wavelet or wavelet packets, to decompose signals into sets of local features with various degrees of sparsity. We use this intrinsic property for selecting the best (most sparse) subsets of features for further separation. The performance of the algorithm is verified on noise-free and noisy data. Experiments with simulated signals, musical sounds and images demonstrate significant improvement of separation quality over previously reported results.

    Item Type: Article
    Keywords: Blind source separation; multinode sparse representation;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5507
    Depositing User: Barak Pearlmutter
    Date Deposited: 15 Oct 2014 14:58
    Journal or Publication Title: Advances in Neural Information Processing Systems 14
    Publisher: 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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