NMF-Based Speech Enhancement Using Bases Update

This letter presents a speech enhancement technique combining statistical models and non-negative matrix factorization (NMF) with on-line update of speech and noise bases. The statistical model-based enhancement methods have been known to be less effective to non-stationary noises while the template...

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Bibliographic Details
Published in:IEEE signal processing letters 2015-04, Vol.22 (4), p.450-454
Main Authors: Kwon, Kisoo, Shin, Jong Won, Kim, Nam Soo
Format: Article
Language:English
Subjects:
Computational modeling
Factorization
Gain
Mathematical models
Noise
Non-negative matrix factorization
on-line bases update
On-line systems
Signal processing
Speech
Speech enhancement
Statistical analysis
statistical model-based enhancement
Training
Vectors
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Summary:This letter presents a speech enhancement technique combining statistical models and non-negative matrix factorization (NMF) with on-line update of speech and noise bases. The statistical model-based enhancement methods have been known to be less effective to non-stationary noises while the template-based enhancement techniques can deal with them quite well. However, the template-based enhancement techniques usually rely on a priori information. To overcome the shortcomings of both approaches, we propose a novel speech enhancement method that combines the statistical model-based enhancement scheme with the NMF-based gain function. For a better performance in time-varying noise environments, both the speech and noise bases of NMF are adapted simultaneously with the help of the estimated speech presence probability. Experimental results showed that the proposed method outperformed not only the statistical model-based and NMF approaches, but also their combination in various noise environments.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2362556