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ICA
2007
Springer

Discovering Convolutive Speech Phones Using Sparseness and Non-negativity

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Discovering Convolutive Speech Phones Using Sparseness and Non-negativity
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 a convolutive NMF algorithm that includes a sparseness constraint on the activations and has multiplicative updates. In combination with a spectral magnitude transform of speech, this method extracts speech phones that exhibit sparse activation patterns, which we use in a supervised separation scheme for monophonic mixtures.
Paul D. O'Grady, Barak A. Pearlmutter
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ICA
Authors Paul D. O'Grady, Barak A. Pearlmutter
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