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Overdetermined Blind Source Separation by Gaussian Mixture Model

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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6839))

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Abstract

The blind separation of overdetermined mixtures, i.e., the case where more sensors than sources are available is considered in this paper. The contrast function for overdetermined blind source separation problem is presented, together with its gradient. An iterative method is proposed to solve the overdetermined blind source separation problem, where Gaussian mixture model is used to estimate the density of the unknown sources. The result of simulation demonstrates the efficiency of the proposed algorithm.

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Author information

Authors and Affiliations

  1. Department of Automation, Shanghai University of Engineering Science, Shanghai, 201620, PR China

    Yujia Wang

  2. School of Electronic and Electrical Engineering, Shanghai Second Polytechnic University, Shanghai, 201209, PR China

    Yunfeng Xue

Authors
  1. Yujia Wang
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  2. Yunfeng Xue
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  4. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    M. Michael Gromiha

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, Y., Xue, Y. (2012). Overdetermined Blind Source Separation by Gaussian Mixture Model. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_21

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  • DOI: https://doi.org/10.1007/978-3-642-25944-9_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25943-2

  • Online ISBN: 978-3-642-25944-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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