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Cumulant-Based Blind Separation of Convolutive Mixtures

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

This paper focuses on the blind source separation of convolutive mixtures based on high order cumulants. It is proved that the zero-forcing of pairwise cross-cumulants of the outputs of separation system is a sufficient criterion for the separation of convolutive mixtures. New algorithm is developed based on this criterion. Simulation results are presented to support the validity of the algorithm.

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References

  1. Bell, A.J., Sejnowski, T.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)

    Article  Google Scholar 

  2. Amari, S., Douglas, S.C., Cichocki, A., Yang, H.H.: Multichannel Blind Deconvolution and Equalization Using the Natural Gradient. In: Proc. IEEE Int. Workshop Wireless Communication, Paris, pp. 101–104 (1997)

    Google Scholar 

  3. Smaragdis, P.: Blind Separation of Convolved Mixtures in the Frequency Domain. Neurocomputing 22, 21–34 (1998)

    Article  MATH  Google Scholar 

  4. Parra, L., Spence, C.: Convolutive Blind Separation of Non-stationary Sources. IEEE Trans. Speech and Audio Processing 8, 320–327 (2000)

    Article  Google Scholar 

  5. Van, G.S., Van, C.D.: Signal Separation by Symmetric Adaptive Decorrelation: Stability, Convergence and Uniqueness. IEEE Trans. on SP 43, 1602–1612 (1995)

    Article  Google Scholar 

  6. Weinstein, E., Feder, M., Oppenheim, A.V.: Multichannel Signal Separation by Decorrelation. IEEE Trans. Speech and Audio Processing 1, 405–413 (1993)

    Article  Google Scholar 

  7. Kawamoto, M., Matsuoka, K.: A Method of Blind Separation for Convolved Non-stationary Signals. Neurocomputing 22, 157–171 (1998)

    Article  MATH  Google Scholar 

  8. Yellin, D., Weinstein, E.: Multichannel Signal Separation: Methods and Analysis. IEEE Trans. Signal Processing 44, 106–118 (1996)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic and Information, Dalian university of technology, Dalian, P.R. China, 116023

    Tiemin Mei & Fuliang Yin

  2. School of Electrical, Computer and Telecommunications Engineering, The University of Wollongong, Australia

    Jiangtao Xi & Joe F. Chicharo

Authors
  1. Tiemin Mei
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  2. Fuliang Yin
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  3. Jiangtao Xi
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  4. Joe F. Chicharo
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Mei, T., Yin, F., Xi, J., Chicharo, J.F. (2004). Cumulant-Based Blind Separation of Convolutive Mixtures. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_110

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_110

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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