Peer-Reviewed Journal Details
Mandatory Fields
O'Grady, PD;Pearlmutter, BA
2008
December
Neurocomputing
Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint
Published
40 ()
Optional Fields
INDEPENDENT COMPONENT ANALYSIS SEPARATION DECONVOLUTION ALGORITHMS INPUTS
72
88
101
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 standard convolutive NMF. (C) 2008 Elsevier B.V. All rights reserved.
AMSTERDAM
0925-2312
10.1016/j.neucom.2008.01.033
Grant Details