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Reference Based Contrast Functions in a Semi-blind Context

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Independent Component Analysis and Signal Separation (ICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5441))

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Abstract

We deal with blind signal extraction in the framework of a convolutive mixture of independent sources. Considering so-called reference signals, we generalize former identifiability conditions. Based on this result, we propose to incorporate some a priori information in the references. We show the validity of reference based contrast functions in two semi-blind situations. The results are confirmed by computer simulations.

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References

  1. Castella, M., Rhioui, S., Moreau, E., Pesquet, J.C.: Quadratic higher-order criteria for iterative blind separation of a MIMO convolutive mixture of sources. IEEE Trans. Signal Processing 55(1), 218–232 (2007)

    Article  MathSciNet  Google Scholar 

  2. Kawamoto, M., Kohno, K., Inouye, Y.: Eigenvector algorithms incorporated with reference systems for solving blind deconvolution of MIMO-IIR linear systems. IEEE Signal Processing Letters 14(12), 996–999 (2007)

    Article  MATH  Google Scholar 

  3. Hua, Y., Tugnait, J.K.: Blind identifiability of FIR-MIMO systems with colored input using second order statistics. IEEE Signal Processing Letters 7(12), 348–350 (2000)

    Article  Google Scholar 

  4. Simon, C., Loubaton, P., Jutten, C.: Separation of a class of convolutive mixtures: a contrast function approach. Signal Processing (81), 883–887 (2001)

    Article  MATH  Google Scholar 

  5. Tugnait, J.K.: Identification and deconvolution of multichannel linear non-gaussian processes using higher order statistics and inverse filter criteria. IEEE Trans. Signal Processing 45(3), 658–672 (1997)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institut TELECOM; TELECOM & Management SudParis, UMR-CNRS 5157, 9 rue Charles Fourier, 91011, Evry CEDEX, France

    Marc Castella

  2. University of Sud Toulon Var, ISITV, LSEET UMR-CNRS 6017, Av. G. Pompidou, BP56, 83162, La Valette du Var CEDEX, France

    Eric Moreau

Authors
  1. Marc Castella
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  2. Eric Moreau
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Electrical Engineering, ITE 324, University of Maryland, Baltimore County, 1000 Hilltop Circle, MD 21250, Baltimore, USA

    Tülay Adali

  2. Domaine Universitaire, GIPSA-lab, BP 46, 38402, Saint Martin d’Hères Cedex, France

    Christian Jutten

  3. Departamento de Microonda e Óptica (DMO), FEEC / Unicamp, Avenida Albert Einstein 400, 13083-852, Campinas, Sao Paulo, Brazil

    João Marcos Travassos Romano

  4. Centro Tecnológico, Curso de Engenharia Elétrica, Universidade Federal do Maranhão, Avenida dos Portugueses, s/n, Bacanga, 65080-040, São Luís, MA, Brazil

    Allan Kardec Barros

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© 2009 Springer-Verlag Berlin Heidelberg

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Castella, M., Moreau, E. (2009). Reference Based Contrast Functions in a Semi-blind Context. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-00599-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00598-5

  • Online ISBN: 978-3-642-00599-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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