Speech enhancement using sparse convolutive non-negative matrix factorization with basis adaptation
Michael A. Carlin, Nicolas Malyska, Thomas F. Quatieri · 2012
We introduce a framework for speech enhancement based on convolutive non-negative matrix factorization that leverages available speech data to enhance arbitrary noisy utterances with no a priori knowledge of the speakers or noise types present. Previous approaches have shown the utility of a sparse recon-struction of the speech-only components of an observed noisy utterance. We demonstrate that an underlying speech represen-tation which, in addition to applying sparsity, also adapts to the noisy acoustics improves overall enhancement quality. The proposed system performs comparably to a traditional Wiener filtering approach, and the results suggest that the proposed framework is most useful in moderate- to low-SNR scenarios. Index Terms: speech enhancement, convolutive non-negative matrix factorization, basis adaptation, sparsity