Discovering convolutive speech phones using sparseness and non-negativity

Authors:
Paul D. O'Grady;Barak A. Pearlmutter
Affiliations:
Complex & Adaptive Systems Laboratory, University College Dublin, Dublin 4, Ireland;Hamilton Institute, National University of Ireland Maynooth, Co. Kildare, Ireland
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

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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), 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.