Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint

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.

• Type: Journal
• Year: 2008
• Where: IJON
• Authors: Paul D. O'Grady, Barak A. Pearlmutter
• Keywords: Classic Convolutive Nmf, IJON 2008, Non-negative Matrix Factorisation, Spectral Magnitude Transform