Convolutive Non-Negative Matrix Factorisation with a Sparseness Constraint

Abstract

Discovering a representation which 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. We present an extension to NMF that is convolutive and includes a sparseness constraint. In combination with a spectral magnitude transform, this method discovers auditory objects and their associated sparse activation patterns.

Cite this paper

@article{OGrady2006ConvolutiveNM, title={Convolutive Non-Negative Matrix Factorisation with a Sparseness Constraint}, author={P. D. O'Grady and B. A. Pearlmutter}, journal={2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing}, year={2006}, pages={427-432} }