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- W195578718 abstract "Discovering a representation that allows auditory data tobe parsimoniously represented is useful for many machine learning and signal processing tasks. 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. Here, we present anextension to convolutive NMF that includes a sparseness constraint. In combination with a spectral magnitude transform of speech, this method extracts speech phones (and their associated sparse activation patterns), which we use in a supervised separation scheme for monophonic mixtures." @default.
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- W195578718 date "2007-01-01" @default.
- W195578718 modified "2023-09-26" @default.
- W195578718 title "Discovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints" @default.
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