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    Discovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints

    O'Grady, Paul D. and Pearlmutter, Barak A. (2007) Discovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints. In: Proceedings of the Seventh International Conference on Independent Component Analysis, September 9-12, 2007, London, UK.

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    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), which is a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint. In combination with a spectral magnitude transform of speech, this method extracts speech phones (and their associated sparse activation patterns), which we use in a supervised separation scheme for monophonic mixtures.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Non-negative Matrix Factorisation (NMF); Convolutive NMF; Sparse Convolutive NMF.
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 1313
    Depositing User: Barak Pearlmutter
    Date Deposited: 25 Mar 2009 17:20
    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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