Wavelet De-noising for Blind Source Separation in Noisy Mixtures

  • Bertrand Rivet
  • Vincent Vigneron
  • Anisoara Paraschiv-Ionescu
  • Christian Jutten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)


Blind source separation, which supposes that the sources are independent, is a well known domain in signal processing. However, in a noisy environment the estimation of the criterion is harder due to the noise. In strong noisy mixtures, we propose two new principles based on the combination of wavelet de-noising processing and blind source separation. We compare them in the cases of white/correlated Gaussian noise.


Discret Wavelet Transform Independent Component Analysis Source Separation Blind Source Separation Decay Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Bertrand Rivet
    • 1
  • Vincent Vigneron
    • 1
  • Anisoara Paraschiv-Ionescu
    • 2
  • Christian Jutten
    • 1
  1. 1.Institut National Polytechnique de GrenobleLaboratoire des Images et des SignauxGrenobleFrance
  2. 2.Swiss Federal Institute of TechnologyLausanneSwitzerland

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