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    Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint


    O'Grady, Paul D. and Pearlmutter, Barak A. (2008) Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint. Neurocomputing, 72 (1-3). pp. 88-101. ISSN 0925-2312

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    Abstract

    Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint, where the resultant algorithm has multiplicative updates and utilises the beta divergence as its reconstruction objective. In combination with a spectral magnitude transform of speech, this method discovers auditory objects that resemble speech phones along with their associated sparse activation patterns. We use these in a supervised separation scheme for monophonic mixtures, finding improved separation performance in comparison to classic convolutive NMF.

    Item Type: Article
    Keywords: Non-negative matrix factorisation; Sparse representations; Convolutive dictionaries; Speech phone analysis; Hamilton Institute.
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 1697
    Identification Number: https://doi.org/10.1016/j.neucom.2008.01.033
    Depositing User: Hamilton Editor
    Date Deposited: 01 Dec 2009 12:09
    Journal or Publication Title: Neurocomputing
    Publisher: Elsevier
    Refereed: Yes
    URI:

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