Improved Semi-Supervised NMF Based Real-Time Capable Speech Enhancement

Yonggang HU  Xiongwei ZHANG  Xia ZOU  Meng SUN  Gang MIN  Yinan LI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E99-A   No.1   pp.402-406
Publication Date: 2016/01/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E99.A.402
Type of Manuscript: LETTER
Category: Speech and Hearing
non-negative matrix factorization,  incremental NMF,  real-time capable,  on-line training,  

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Nonnegative matrix factorization (NMF) is one of the most popular tools for speech enhancement. In this letter, we present an improved semi-supervised NMF (ISNMF)-based speech enhancement algorithm combining techniques of noise estimation and Incremental NMF (INMF). In this approach, fixed speech bases are obtained from training samples offline in advance while noise bases are trained on-the-fly whenever new noisy frame arrives. The INMF algorithm is adopted for noise bases learning because it can overcome the difficulties that conventional NMF confronts in online processing. The proposed algorithm is real-time capable in the sense that it processes the time frames of the noisy speech one by one and the computational complexity is feasible. Four different objective evaluation measures at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed method over traditional semi-supervised NMF (SNMF) and well-known robust principal component analysis (RPCA) algorithm.