Adaptive approach to Blind Source Separation

Blind separation of sources (Independent Component Analysis)

• The task is to separate an unknown number N of unknown sources S from M measured linear mixtures of signals.

• For solving the blind source separation problem we have developed a multi-layer ANN learning algorithm with a biologically justified local learning rule.

  See the paper published at NOLTA 1995.

  Alternatively, a robust global learning rule (Amari:Cichocki:Yang:NIPS95) can be applied too.

• An example of blind separation of image sources if M=N=5:
  

  These are the original images which are unknown to the neural net:

  MONO JPEG (121 kB). or COLOR JPEG (149 kB).
  

  Here are five mixed images from the input of the neural net:

  MONO JPEG (168 kB). or COLOR JPEG (215 kB).

  A blind separation process for above five mixtures of four natural images and additional noise image is demonstrated by following movies:

  1. MONOMPEG.
  2. COLORMPEG.

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  W. Kasprzak, Jan. 1996