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BSS with Corrupted Data in Transformed Domains

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Latent Variable Analysis and Signal Separation (LVA/ICA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10169))

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Abstract

Most techniques of Blind Source Separation (BSS) are highly sensitive to the presence of gross errors while these last are ubiquitous in many real-world applications. This mandates the development of robust BSS methods, especially to handle the determined case for which there is currently no strategy able to separate the outliers from the sources contributions. We propose a new method which exploits the difference of structural contents that is naturally exhibited by the sources and the outliers in many applications to accurately separate the two contributions. More precisely, we exploit the sparse representations of the signals in two adapted and different dictionaries to estimate jointly the mixing matrix, the sources and the outliers. Preliminary results show the good accuracy of the proposed algorithm in various settings.

J. Bobin—This work is supported by the European Community through the grants PHySIS (contract no. 640174), DEDALE (contract no. 665044) and LENA (ERC StG no. 678282) within the H2020 Framework Program.

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Authors and Affiliations

  1. CEA, IRFU, Service D’Astrophysique - SEDI, 91191, Gif-sur-Yvette Cedex, France

    Cécile Chenot & Jérôme Bobin

Authors
  1. Cécile Chenot
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  2. Jérôme Bobin
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Corresponding author

Correspondence to Cécile Chenot .

Editor information

Editors and Affiliations

  1. Institute of Information Theory and Automation, Prague, Czech Republic

    Petr Tichavský

  2. Sharif University of Technology, Tehran, Iran

    Massoud Babaie-Zadeh

  3. Grenoble-Alpes University, Grenoble, France

    Olivier J.J. Michel

  4. Toulon University, Toulon, France

    Nadège Thirion-Moreau

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Chenot, C., Bobin, J. (2017). BSS with Corrupted Data in Transformed Domains. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_51

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  • DOI: https://doi.org/10.1007/978-3-319-53547-0_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53546-3

  • Online ISBN: 978-3-319-53547-0

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