SPEECH ENHANCEMENT BASED ON SPARSE THEORY UNDER NOISY ENVIRONMENT

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Proceeding > Item 987654321/103467

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Title:	SPEECH ENHANCEMENT BASED ON SPARSE THEORY UNDER NOISY ENVIRONMENT
Authors:	Hsieh, Ching-Tang;Chen, Yan-heng;Chen, Ting-Wen;Chen, Li-Ming
Contributors:	電機工程學系暨研究所
Keywords:	Speech enhancement, sparse representations, K-SVD, discrete cosine transform (DCT), orthogonal matching pursuit (OMP)
Date:	2015-07-18
Issue Date:	2015-07-27 14:05:46 (UTC+8)
Publisher:	Academy of Taiwan Information Systems Research (ATISR)
Abstract:	Recently, the sparse algorithm for sparse enhancement is more and more popular issues. In this paper, we classify the process of the sparse theory to enhance speech signal into two parts, one is for dictionary training part and the other is signal reconstruction part. We focus on the White Gaussian Noise. Clean speech dictionary D is trained by K-SVD algorithm. The orthogonal matching pursuit(OMP) algorithm is used to obtain the sparse coefficients X of clean speech dictionary D. Denoising performance of the experiments shows that our proposed method is superior than other methods in SNR, LLR, SNRseg and PESQ.
Relation:	International Conference on Internet Studies (NETs 2015)
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Proceeding

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