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Dependent Source Separation with Nonparametric Non-Gaussianity Measure

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

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Abstract

Separating statistically dependent source signals from their linear mixtures is a challenging problem in signal processing society. Firstly, we show that maximization of the non-Gaussianity (NG) measure among the separated signals can realize dependent source signals separation. Then, based on cumulative distribution function (CDF) instead of traditional probability density function (PDF), the NG measure is defined by utilizing statistical distances between different distributions. After that, the CDF based objective function is estimated by utilizing nonparametric order statistics (OS). At last, by consulting the stochastic gradient rule of constrained optimization problem, the efficiently nonparametric dependent sources separation algorithm is derived and termed as nonpNG. Simulation results demonstrate the validity of the proposed statistically dependent sources separation algorithm.

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Acknowledgments

This research is financially supported by the National Natural Science Foundation of China (No. 61401401, 61402421, 61571401) and the China Postdoctoral Science Foundation (No. 2015T80779, 2014M561998).

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Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Fasong Wang & Li Jiang

  2. School of Sciences, Henan University of Technology, Zhengzhou, China

    Rui Li

Authors
  1. Fasong Wang
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  2. Li Jiang
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  3. Rui Li
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Corresponding author

Correspondence to Fasong Wang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Wang, F., Jiang, L., Li, R. (2017). Dependent Source Separation with Nonparametric Non-Gaussianity Measure. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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