Speech Enhancement based on Sparse Representation under Color Noisy Environment

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Title:	Speech Enhancement based on Sparse Representation under Color Noisy Environment
Authors:	Ching-Tang Hsieh, Piao-Yu Huang, Ting-Wen Chen, Yan-heng Chen
Keywords:	Speech enhancement, sparse representations, K-SVD, discrete cosine transform (DCT), orthogonal matching pursuit (OMP)
Date:	2015-11-10
Issue Date:	2016-08-18 13:39:17 (UTC+8)
Abstract:	Recently, sparse algorithm for signal enhancement is more and more popular issues. In this paper, we apply it to enhance speech signal. The process of sparse theory is classified into two parts, one is for dictionary training part and the other is signal reconstruction part. We focus environment on both white Gaussian noise and color noise filtering based on sparse. The orthogonal matching pursuit (OMP) algorithm is used to optimize the sparse coefficients X of clean speech dictionary, where clean speech dictionary is trained by K-SVD algorithm. Then, we multiply these two matrixes D' and X' to reconstruct the clean speech signal. Denoising performance of the experiments shows that our proposed method is superior to other state of art methods in four kinds of objective quality measures as SNR, LLR, SNRseg and PESQ.
Relation:	International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2015. pp.134-138.
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Proceeding

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