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The Research and Simulation of Blind Source Separation Algorithm


  • Tao Gao

    (Department of Automation, North China Electric Power University, Baoding, China)

  • Jincan Li

    (Udutech, Inc., Shijiazhuang, China)


When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

Suggested Citation

  • Tao Gao & Jincan Li, 2016. "The Research and Simulation of Blind Source Separation Algorithm," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), IGI Global, vol. 8(3), pages 1-36, July.
  • Handle: RePEc:igg:japuc0:v:8:y:2016:i:3:p:1-36

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