Speech denoising using nonnegative matrix factorization with priors

Abstract

We present a technique for denoising speech using nonnegative matrix factorization (NMF) in combination with statistical speech and noise models. We compare our new technique to standard NMF and to a state-of-the-art Wiener filter implementation and show improvements in speech quality across a range of interfering noise types.

DOI: 10.1109/ICASSP.2008.4518538

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Speech processing, transmission and quality aspects (stq); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithms

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