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Convolutive non-negative matrix factorisation with a sparseness constraint
Pearlmutter, Barak A.; O'Grady, Paul D.
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.
Keyword(s): Computer Science; 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.
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Type: Conference item
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Pearlmutter, Barak A. and O'Grady, Paul D. (2006) 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.
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First Indexed: 2020-01-31 06:08:09 Last Updated: 2020-04-02 07:46:49