Pearlmutter, Barak A. and O'Grady, Paul D.
Convolutive non-negative matrix factorisation with a sparseness constraint.
In: Proceedings of the 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, 6th - 8th September, 2006, Arlington, VA.
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.
Conference or Workshop Item
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||Audio signal processing; Convolution; Matrix decomposition; Signal representation; Sparse matrices; Spectral analysis; Transforms; Auditory data representation; Machine learning; Nonnegative matrix factorisation convolution; Signal processing; Sparseness constraint; Spectral magnitude transform.
||Science & Engineering > Computer Science
Dr. Barak Pearlmutter
||18 May 2009 12:13
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