O'Grady, Paul D. and Pearlmutter, Barak A.
Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint.
Neurocomputing, 72 (1-3).
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
||Non-negative matrix factorisation; Sparse representations; Convolutive; Hamilton Institute;
dictionaries; Speech phone analysis;
||Science & Engineering > Hamilton Institute
||25 Nov 2009 12:50
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