کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408873 | 679047 | 2008 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint](/preview/png/408873.png)
Discovering a representation that 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. Here, we present an extension to convolutive NMF that includes a sparseness constraint, where the resultant algorithm has multiplicative updates and utilises the beta divergence as its reconstruction objective. In combination with a spectral magnitude transform of speech, this method discovers auditory objects that resemble speech phones along with their associated sparse activation patterns. We use these in a supervised separation scheme for monophonic mixtures, finding improved separation performance in comparison to standard convolutive NMF.
Journal: Neurocomputing - Volume 72, Issues 1–3, December 2008, Pages 88–101