Convolutive Non-negative Matrix Factorisation with Sparseness Constraint


Discovering a parsimonious representation that reflects the structure of audio is a requirement of many machine learning and signal processing methods. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. We present an extension to NMF that is convolutive and forces a sparseness constraint. Combined with spectral magnitude analysis of audio, this method discovers auditory objects and their associated sparse activation patterns.

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@inproceedings{OGrady2006ConvolutiveNM, title={Convolutive Non-negative Matrix Factorisation with Sparseness Constraint}, author={Paul D. O’Grady and Barak A. Pearlmutter}, year={2006} }