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Compressed speech signal sensing based on the structured block sparsity with partial knowledge of support

  • Published:
Journal of Electronics (China)

Abstract

Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper. The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed l 2 / l 1 program and Basis Pursuit (BP). The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals. It is proved by the simulation results of the l 2 / reweighted l 1 mixed program which is an improved version of the mixed l 2 / l 1 program. The second one is the well known large DCT coefficients of voiced speech focus on low frequency. In line with this feature, a special Gaussian and Partial Identity Joint (GPIJ) matrix is constructed as the sensing matrix for voiced speech signals. Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.

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Authors and Affiliations

  1. College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Yunyun Ji & Qian Xu

  2. Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts & Telecommunications, Nanjing, 210003, China

    Yunyun Ji & Zhen Yang

  3. No. 66 Xinmofan Road, Nanjing, 210003, China

    Yunyun Ji

Authors
  1. Yunyun Ji
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  2. Zhen Yang
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  3. Qian Xu
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Corresponding author

Correspondence to Yunyun Ji.

Additional information

Supported by the National Natural Science Foundation of China (No. 60971129), the National Research Program of China (973 Program) (No. 2011CB302303), and the Scientific Innovation Research Program of College Graduate in Jiangsu Province (No. CXLX11_0408).

Communication author: Ji Yunyun, born in 1988, Ph.D..

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Ji, Y., Yang, Z. & Xu, Q. Compressed speech signal sensing based on the structured block sparsity with partial knowledge of support. J. Electron.(China) 29, 62–71 (2012). https://doi.org/10.1007/s11767-012-0761-7

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  • DOI: https://doi.org/10.1007/s11767-012-0761-7

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