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    Survey of Sparse and Non-Sparse Methods in Source Separation


    O'Grady, Paul D. and Pearlmutter, Barak A. and Rickard, Scott T. (2005) Survey of Sparse and Non-Sparse Methods in Source Separation. International Journal of Imaging Systems and Technology, 15 (1). pp. 18-33. ISSN 0899-9457

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    Abstract

    Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ‘‘blind’. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous non-sparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing nonsparse methods, providing insights and appropriate hooks into theliterature along the way.

    Item Type: Article
    Keywords: Blind Sources Separation; sparse methods; Nonnegative Matrix Factorization;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5502
    Identification Number: https://doi.org/10.1002/ima.20035
    Depositing User: Barak Pearlmutter
    Date Deposited: 15 Oct 2014 11:02
    Journal or Publication Title: International Journal of Imaging Systems and Technology
    Publisher: Wiley Periodicals
    Refereed: Yes
    Funders: Science Foundation Ireland grant 00/PI.1/C067
    URI:

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