14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Speech Enhancement Using Convolutive Nonnegative Matrix Factorization with Cosparsity Regularization

Majid Mirbagheri, Yanbo Xu, Sahar Akram, Shihab Shamma

University of Maryland at College Park, USA

A novel method for speech enhancement based on Convolutive Non-negative Matrix Factorization (CNMF) is presented in this paper. The sparsity of activation matrix for speech components has already been utilized in NMF-based enhancement methods. However such methods do not usually take into account prior knowledge about occurrence relations between different speech components. By introducing the notion of cosparsity, we demonstrate how such relations can be characterized from available speech data and enforced when recovering speech from noisy mixtures. Through objective evaluations we show our proposed regularization improves sparse reconstruction of speech, especially in low SNR conditions.

Full Paper

Bibliographic reference.  Mirbagheri, Majid / Xu, Yanbo / Akram, Sahar / Shamma, Shihab (2013): "Speech enhancement using convolutive nonnegative matrix factorization with cosparsity regularization", In INTERSPEECH-2013, 456-459.