A Continuous Time Balanced Parametrization Approach to Multichannel Blind Deconvolution

  • Liang Suo Ma
  • Ah Chung Tsoi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

In this paper, we will apply a balanced parametrization approach to multichannel blind deconvolution problem with the mixer being modelled as a continuous time linear time invariant system. Such an approach has the advantages of (a) being a computationally robust method, compared with the controller canonical form representation or observer canonical form representation of the linear time invariant continuous time system, and (b) allowing the determination of the number of states required in the demixer. Our approach is validated through a computer simulation example using speech signals.

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References

  1. 1.
    Amari, S.: Natural gradient working efficiently in learning. Neural Computation 10, 251–276 (1998)CrossRefGoogle Scholar
  2. 2.
    Chou, C.T., Maciejowski, J.M.: System identification using balanced parametrizations. IEEE Trans. Auto. Contr. 42, 956–974 (1997)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, Chichester (2002)CrossRefGoogle Scholar
  4. 4.
    Erten, G., Salam, F.: Voice extraction by on-line signal separation and recovery. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 46, 915–922 (1998)CrossRefGoogle Scholar
  5. 5.
    Glover, K.: All optimal Hankel norm approximations of linear multivariable systems and L∞ error bounds. International Journal of Control 39, 1115–1193 (1984)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Kailath, T.: Linear Systems. Prentice-Hall, Englewood Cliffs (1980)MATHGoogle Scholar
  7. 7.
    Tsoi, A.C., Ma, L.S.: Blind Deconvolution of Dynamical System Using a Balanced Parameterised State Space Approach. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hongkong, vol. IV, pp. 309–312 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Liang Suo Ma
    • 1
  • Ah Chung Tsoi
    • 1
  1. 1.Office of Pro-Vice Chancellor (IT)University of WollongongWollongongAustralia

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