Overdetermined Blind Source Separation by Gaussian Mixture Model

  • Yujia Wang
  • Yunfeng Xue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6839)


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.


Gaussian Mixture Model Independent Component Analysis Blind Source Separation Blind Signal Gradient Descent Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Comon, P., Jutten, C., Herault, J.: Blind separation of sources, part II: problems statement. Signal Processing 24(1), 11–20 (1991)CrossRefzbMATHGoogle Scholar
  2. 2.
    Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: learning algorithms and applications. John Wiley and Sons, Chichester (2002)CrossRefGoogle Scholar
  3. 3.
    Joho, M., Mathis, H., Lambert, R.: Overdetermined blind source separation: Using more sensors than source signals in a noisy mixture. In: Proc. International Conference on Independent Component Analysis and Blind Signal Separation, Citeseer, pp. 81–86 (2000)Google Scholar
  4. 4.
    Zhang, L., Cichocki, A., Amari, S.: Natural gradient algorithm for blind separation of overdetermined mixture with additive noise. IEEE Signal Processing Letters 6(11), 293–295 (1999)CrossRefGoogle Scholar
  5. 5.
    Golub, G.H., Van Loan, C.F.: Matrix computations. Johns Hopkins Univ. Press, Baltimore (1996)zbMATHGoogle Scholar
  6. 6.
    Zhu, X., Zhang, X., Ye, J.: A Generalized Contrast Function and Stability Analysis for Overdetermined Blind Separation of Instantaneous Mixtures. Neural Computation 18(3), 709–728 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Xue, Y., Wang, Y., Yang, J.: Independent component analysis based on gradient equation and kernel density estimation. Neurocomputing 72(7-9), 1597–1604 (2009)CrossRefGoogle Scholar
  8. 8.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2000)zbMATHGoogle Scholar
  9. 9.
  10. 10.
    Amari, S., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 757–763. MIT Press, Cambridge (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yujia Wang
    • 1
  • Yunfeng Xue
    • 2
  1. 1.Department of AutomationShanghai University of Engineering SciencePR China
  2. 2.School of Electronic and Electrical EngineeringShanghai Second Polytechnic UniversityPR China

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