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A second-order blind source separation method for bilinear mixtures

Authors
  • Jarboui, Lina1, 2
  • Deville, Yannick1
  • Hosseini, Shahram1
  • Guidara, Rima2
  • Hamida, Ahmed Ben2
  • Duarte, Leonardo T.3
  • 1 Toulouse University, CNRS-OMP, Institut de Recherche en Astrophysique et Planétologie (IRAP), Toulouse, France , Toulouse (France)
  • 2 Sfax University, ENIS, Advanced Technologies for Medicine and Signals (ATMS), Sfax, Tunisia , Sfax (Tunisia)
  • 3 University of Campinas (UNICAMP), School of Applied Sciences, Limeira, Brazil , Limeira (Brazil)
Type
Published Article
Journal
Multidimensional Systems and Signal Processing
Publisher
Springer US
Publication Date
May 06, 2017
Volume
29
Issue
3
Pages
1153–1172
Identifiers
DOI: 10.1007/s11045-017-0493-9
Source
Springer Nature
Keywords
License
Yellow

Abstract

In this paper, we are interested in the problem of Blind Source Separation using a Second-order Statistics (SOS) method in order to separate autocorrelated and mutually independent sources mixed according to a bilinear (BL) model. In this context, we propose a new approach called Bilinear Second-order Blind Source Separation, which is an extension of linear SOS methods, devoted to separate sources present in BL mixtures. These sources, called extended sources, include the actual sources and their products. We first study the statistical properties of the different extended sources, in order to verify the assumption of identifiability when the actual sources are zero-mean and when they are not. Then, we present the different steps performed in order to estimate these actual centred sources and to extract the actual mixing parameters. The obtained results using artificial mixtures of synthetic and real sources confirm the effectiveness of the new proposed approach.

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