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.

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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; Hamilton Institute; dictionaries; Speech phone analysis;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	1685
Depositing User:	Hamilton Editor
Date Deposited:	25 Nov 2009 12:50
Journal or Publication Title:	Neurocomputing
Publisher:	Elsevier
Refereed:	Yes
URI:
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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