Conference Publication Details
Mandatory Fields
O'Grady P.;Pearlmutter B.
Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Convolutive non-negative matrix factorisation with a sparseness constraint
2007
December
Published
1
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Optional Fields
427
432
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. © 2006 IEEE.
10.1109/MLSP.2006.275588
Grant Details