Identification of Mixing Matrix in Blind Source Separation

  • Xiaolu Li
  • Zhaoshui He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


Blind identification of mixing matrix approach and the corresponding algorithm are proposed in this paper. Usually, many conventional Blind Source Separation (BSS) methods separate the source signals by estimating separated matrix. Different from this way, we present a new BSS approach in this paper, which achieves BSS by directly identifying the mixing matrix, especially for underdetermined case. Some experiments are conducted to check the validity of the theory and availability of the algorithm in this paper.


Independent Component Analysis Blind Source Separation Neural Computation Iteration Formula Voice Signal 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amari, S.: Natural Gradient Works Efficiently in Learning. Neural Computation 10(2), 251–276 (1998)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Amari, S., Cichocki, A., Yang, H.: A New Learning Algorithm for Blind Signal Separation. In: Touretzky, D.S., Mozer, C.M., Hasselmo, M.E. (eds.) Advances in neural information processing systems, vol. 8, pp. 757–763. MIT Press, Cambridge (1996)Google Scholar
  3. 3.
    Bell, A.J., Sejnowski, T.J.: An Information-maxization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7(6), 1129–1159 (1995)CrossRefGoogle Scholar
  4. 4.
    Belouchrani, A., Cardoso, J.F.: Maximum Likelihood Source Separation for Discrete Sources. In: Proc. EUSIPCO, pp. 768–771 (1994)Google Scholar
  5. 5.
    Bofill, P., Zibulevsky, M.: Underdetermined Source Separation Using Sparse Representa-tions. Signal processing 81, 2353–2362 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Cardoso, J.F., Laheld, B.H.: Equivariant Adaptive Source Separation. IEEE Trans. Signal Processing 44(12), 3017–3030 (1996)CrossRefGoogle Scholar
  7. 7.
    Comon, P.: Independent Component Analysis, a New Concept? Signal Processing 36, 287–314 (1994)MATHCrossRefGoogle Scholar
  8. 8.
    Girolami, M.: A Variational Method for Learning Sparse and Overcomplete Representation. Neural Computation 13(11), 2517–2532 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent Component Analysis Using an Ex-tended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources. Neural Computation 11(2), 417–441 (1999)CrossRefGoogle Scholar
  10. 10.
    Lee, T.W., Lewicki, M.S., et al.: Blind Source Separation of More Sources than Mixtures Using Overcomplete Representations. IEEE signal processing letters 4, 87–90 (1999)Google Scholar
  11. 11.
    Lewicki, M.S., Sejnowski, T.J.: Learning Overcomplete Representations. Neural Computation 12(2), 337–365 (2000)CrossRefGoogle Scholar
  12. 12.
    Li, Y., Cichocki, A., Amari, S.: Analysis of Sparse Representation and Blind Source Separation. Neural Computation 16(6), 1193–1234 (2004)MATHCrossRefGoogle Scholar
  13. 13.
    Li, Y., Cichocki, A., Zhang, L.: Blind Source Estimation of FIR Channels for Binary Sources: a Grouping Decision Approach. Signal Processing 84(12), 2245–2263 (2004)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Wang, J., Cichocki, A.: Blind Source Extraction from Convolutive Mixtures in III-conditioned Multi-input Multi-output Channels. IEEE Transactions on Circuits and Systems I 51(9), 1814–1822 (2004)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Sanches, A.V.D.: Frontiers of Research in BSS/ICA. Neurocomputing 49, 7–23 (2002)CrossRefGoogle Scholar
  16. 16.
    Tong, L., Liu, R., Soon, V.C.: Indeterminacy and Identificability of Blind Identifica-tion. IEEE Trans. Circuits Syst. 38(5), 499–509 (1991)MATHCrossRefGoogle Scholar
  17. 17.
    Zibulevsky, M., Pearlmutter, B.A.: Blind Source Separation by Sparse Decomposition in a Signal Dictionary. Neural Computation 13(4), 863–882 (2001)MATHCrossRefGoogle Scholar
  18. 18.
    Xie, S., Zhang, J.: Blind separation algorithm of minimal mutual information based on rotating transform. Tien Tzu Hsueh Pao/Acta Electronica Sinica 30(5), 628–631 (2002)Google Scholar
  19. 19.
    Xiao, M., Xie, S., Fu, Y.: A Novel Approach for Underdetermined Blind Source in the Frequency Domain. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 484–489. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Xie, S., Xiao, M., Fu, Y.: A Novel Approach for Underdetermined Blind Speech Signal Separation. In: DCDIS Proceedings 3: Impulsive Dynamical Systems and Applications, pp. 1846–1853 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaolu Li
    • 1
  • Zhaoshui He
    • 1
  1. 1.School of Electronics and Information EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations