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Blind separation of linear-quadratic mixtures of real sources using a recurrent structure

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Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

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Abstract

In this paper, we propose an approach for separating linearquadratic mixtures of independent real sources. The method is based on parametric identification of a recurrent separating structure by means of an adaptive algorithm which uses the higher-order statistics of the outputs of this structure. We study the local stability of the recurrent structure and show experimentally that when it is stable at the separating point, it succeeds in separating the sources.

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Author information

Authors and Affiliations

  1. Laboratoire d’Acoustique, Métrologie, Instrumentation, Université Paul Sabatier, Bat. 3R1B2, 118 route de Narbonne, 31062, Toulouse Cedex, France

    Shahram Hosseini & Yannick Deville

Authors
  1. Shahram Hosseini
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  2. Yannick Deville
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Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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Hosseini, S., Deville, Y. (2003). Blind separation of linear-quadratic mixtures of real sources using a recurrent structure. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_31

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  • DOI: https://doi.org/10.1007/3-540-44869-1_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

  • eBook Packages: Springer Book Archive

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