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
||Copyright Notice "©2006 IEEE. Reprinted from Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4053687&isnumber=4053605
||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.
||Faculty of Science and Engineering > Computer Science
||18 May 2009 12:13
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