NDLI: Adaptive speech enhancement using sparse prior information

Content Provider	IEEE Xplore Digital Library
Author	Zhimin Xiang Yuantao Gu
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Zhimin Xiang; Yuantao Gu)
Abstract	In recent years, sparse representation is adopted to improve the quality of noise corrupted speech. However, the representation of noise is also found to be sparse in some special cases, which degrades the performance of sparsity based speech enhancement. An adaptive speech enhancement algorithm using sparse prior information is proposed in this paper. In the proposed method, speech enhancement is casted to an optimization problem, where linear prediction (LP) residual and DCT coefficients are combined and adopted as the representation of speech to ensure that noise is dense in the such domain. Other features, including speech energy, noise energy, and interframe correlation are also considered as constraints to improve the quality and intelligibility of recovered speech. Experiment results show that the proposed algorithm exceeds the reference algorithms in various noise scenarios, especially, in the cases of narrowband noise and low SNR.
Starting Page	7025
Ending Page	7029
File Size	163786
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639024
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Noise Speech enhancement Discrete cosine transforms Optimization Signal processing algorithms Correlation interframe correlation Adaptive speech enhancement sparse representation linear prediction energy constraint
Content Type	Text
Resource Type	Article

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