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Multichannel Speech Separation Using Adaptive Parameterization of Source PDFs

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

Convolutive and temporally correlated mixtures of speech are tackled with an LP-based temporal pre-whitening stage combined with the natural gradient algorithm (NGA), to essentially perform spatial separation by maximizing entropy at the output of a nonlinear function. In the past, speech sources have been parameterized by the generalized Gaussian density (GGD) model, in which the exponent parameter directly relates to the exponent of the corresponding optimal nonlinear function. In this paper, we present an adaptive, source dependent estimation of this parameter, controlled exclusively by the statistics of the output source estimates. Comparative experimental results illustrate the inherent flexibility of the proposed method, as well as an overall increase in convergence speed and separation performance over existing approaches.

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References

  1. Amari, S., Cichocki, A., Yang, H.: A New Learning Algorithm for Blind Signal Separation. In: Advances in Neural Information Processing Systems 8, pp. 757–763. MIT Press, Cambridge (1996)

    Google Scholar 

  2. Bell, A., Sejnowski, T.: An Information Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7(6), 1129–1159 (1995)

    Article  Google Scholar 

  3. Choi, S., Cichocki, A., Amari, S.: Flexible Independent Component Analysis. Journal of VLSI Signal Processing 26(1), 25–38 (2000)

    Article  Google Scholar 

  4. Do, M.N., Vetterli, M.: Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance. IEEE Trans. on Image Processing 11(2), 146–158 (2002)

    Article  MathSciNet  Google Scholar 

  5. Gazor, S., Zhang, W.: Speech Probability Distribution. IEEE Signal Processing Letters 10(7), 204–207 (2003)

    Article  Google Scholar 

  6. Kokkinakis, K., Zarzoso, V., Nandi, A.K.: Blind Separation of Acoustic Mixtures based on Linear Prediction Analysis. In: Proc. Fourth Int. Symp. on ICA and BSS, Nara, Japan, April 1-4, pp. 343–348 (2003)

    Google Scholar 

  7. Kokkinakis, K., Nandi, A.K.: Optimal Blind Separation of Convolutive Audio Mixtures without Temporal Constraints. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Montreal, Canada, May 17-21, pp. 217–220 (2004)

    Google Scholar 

  8. Kokkinakis, K., Nandi, A.K.: Multichannel Blind Deconvolution for Source Separation in Convolutive Mixtures of Speech. Submitted to IEEE Trans. on Speech and Audio Processing (February 2004)

    Google Scholar 

  9. Lambert, R.H.: Multichannel Blind Deconvolution: FIR Matrix Algebra and Separation of Multipath Mixtures. Ph.D. Thesis, University of Southern California (May 1996)

    Google Scholar 

  10. Lambert, R.H., Bell, A.J.: Blind Separation of Multiple Speakers in a Multipath Environment. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Munich, Germany, April 21-24, pp. 423–426 (1997)

    Google Scholar 

  11. Lee, T.-W., Bell, A.J., Orglmeister, R.: Blind Source Separation of Real World Signals. In: Proc. ICNN, Texas, June 9-12, pp. 2129–2135 (1997)

    Google Scholar 

  12. Lee, T.-W., Girolami, M., Sejnowski, T.: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources. Neural Computation 11(2), 417–441 (1999)

    Article  Google Scholar 

  13. Mathis, H., von Hoff, T.P., Joho, M.: Blind Separation of Signals with Mixed Kurtosis Signs Using Threshold Activation Functions. IEEE Trans. on Neural Networks 12(3), 618–624 (2001)

    Article  Google Scholar 

  14. Varanasi, M.K., Aazhang, B.: Parametric Generalized Gaussian Density Estimation. J. Acoust. Soc. America 86(4), 1404–1415 (1989)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, Liverpool, L69 3GJ, UK

    Kostas Kokkinakis & Asoke K. Nandi

Authors
  1. Kostas Kokkinakis
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  2. Asoke K. Nandi
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Kokkinakis, K., Nandi, A.K. (2004). Multichannel Speech Separation Using Adaptive Parameterization of Source PDFs. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_62

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

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