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Speech Separation Based on Higher Order Statistics Using Recurrent Neural Networks

  • Yan Li
  • David M. W. Powers
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 14)

Abstract

Independent Component Analysis (ICA) (Comon, 1994; Lee, 1998; Karhunen et al,1997; Haykin, 1998) is an unsupervised technique, which tries to represent the data in terms of statistically independent variables. ICA and the related blind source separation (BSS) and application topics both in unsupervised neural learning and statistical signal processing.

Keywords

Independent Component Analysis Recurrent Neural Network Independent Component Analysis Blind Source Separation Processing Node 
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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References

  1. Bell, A. J. and T. J. Sejnowski (1995). An information-maximisation approach to blind separation and blind deconvolution, Neural Computation, 7 (6), 1004–1034.CrossRefGoogle Scholar
  2. Cardoso J.F. (1989) Source Separation Using Higher Order Moments, IEEE Transactions on Signal Processing, Vol. No. pp. 2109–2112Google Scholar
  3. Cardoso JF and Comon P (1996). Independent component analysis, a survey of some algebraic methods. In Proc. ISCAS’96, vol. 2, pp. 93–96.Google Scholar
  4. Chan, D. C. B. (1997). Blind Signal Processing, PhD thesis, Signal Processing and Communications Laboratory Department of Engineering, University of Cambridge, UK.Google Scholar
  5. Comon P. (1994), Independent component analysis-a new concept?, signal processing, vol. 36, pp. 287–314.MATHGoogle Scholar
  6. Haykin S. (1998), Neural networks- A comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  7. Karhunen J., Oja E., etc. (1997), A class of neural networks for independent component analysis, IEEE Trans. On Neural Networks, vol. 8, pp. 486–504.CrossRefGoogle Scholar
  8. Lee T. W. (1998), Independent component analysis-theory and application, Dordrecht, The Netherlands: Kluwer.Google Scholar
  9. Li Y., Powers D. and Wen P. (2001), Separation and Deconvolution of Speech Using Recurrent Neural Networks, pp. 1303–1309, Vol. III, Proceedings of the International Conference on Artificial Intelligence (IC-AI’01), June 25–28, 2001, Las Vegas, Nevada, USA.Google Scholar
  10. Lin J., Grier D. and Cowan J. (1997), Faithful representation of separable distributions, Neural Computation, Vol. 9, pp. 1305–1320.CrossRefGoogle Scholar
  11. Mansour A and Jutten C (1995) Fourth Order Criteria for Blind Sources Separation. IEEE Transactions on Signal Processing, Vol. 43, No. 8 August 1995. pp. 2022–2025.CrossRefGoogle Scholar
  12. Mansour A. and Ohnishi N. (1999), Multichannel Blind Separation of Sources Algorithm Based on Cross- Cumulant and the Levenberg-Marquardt Method, Vol. 47, No. 11, November, pp. 3172–3179.Google Scholar
  13. Nguyen Thi H.L. and Jutten C. (1995) Blind Source Separation for Convolutive Mixtures, Signal Processing Vol. 45, pp. 209–229.MATHCrossRefGoogle Scholar
  14. Schobben, D. (1999), Efficient adaptive multi-channel concepts in acoustics: Blind signal separation and echo cancellation, PhD thesis.Google Scholar
  15. Vicente Zarzoso, Asoke K. Nandi (1999), Blind Separation of Independent sources for Virtually Any Source Probability Density Function, IEEE transactions on signal processing, Vol. 47, No. 9, 2419–2432.CrossRefGoogle Scholar
  16. Westner, A. G. (1999). Object based audio capture: Separating acoustically mixed sounds. Master’s thesis, MIT.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Yan Li
    • 1
  • David M. W. Powers
    • 2
  1. 1.University of Southern QueenslandAustralia
  2. 2.Flinders University of South AustraliaAustralia

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