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Volume 23 Issue 4
Apr.  2001
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He Zhenya, Yang Luxi, Liu Ju, Lu Ziyi, He Chen. A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION[J]. Journal of Electronics & Information Technology, 2001, 23(4): 345-353.
Citation: He Zhenya, Yang Luxi, Liu Ju, Lu Ziyi, He Chen. A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION[J]. Journal of Electronics & Information Technology, 2001, 23(4): 345-353.

A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION

  • Received Date: 1999-04-14
  • Rev Recd Date: 1999-10-13
  • Publish Date: 2001-04-19
  • A class of learning algorithms is drived for blind separation of independent source signals in this paper. These algorithms are based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. By utilizing the technique of multivariate density esti-mation, two types of separating algorithms are obtained. Simulations illustrate the effectiveness of the algorithms.
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