文章基本信息

标题：SPEECH SIGNAL COMPRESSED SENSING BASED ON K-SVD ADAPTIVE DICTIONARY
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作者：YAN ZHOU ; HEMING ZHAO
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：48
期号：2
页码：1237-1243
出版社：Journal of Theoretical and Applied
摘要：This paper proposes a novel and successful method for speech signal compressed sensing based on K-Singular Value Decomposition (K-SVD) algorithm. K-SVD is an iterative method that alternates between sparse representation of the train samples based on the current dictionary and a process of updating the dictionary atoms to better fit the speech data. The presented K-SVD algorithm is applied here for training an adaptive over complete dictionary which can best suit a set of given speech signals. The sparse coefficients can be obtained by conducting the Orthogonal Matching Pursuit(OMP) sparse decomposition algorithm. At last, the original signal can be reconstructed by exploiting reconstruction algorithm. The experimental results show that, compared with the traditional basis function dictionary, the proposed method has a stronger adaptability which can be availably used for speech signal sparse representation. Moreover, the compressed sensing based on K-SVD algorithm achieves higher reconstruction accuracy and better denoising efficiency.
关键词：Speech Signal; Compressed Sensing; K-SVD Algorithm; Overcomplete Dictionary; Sparse Decomposition; Signal Reconstruction