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Blind source separation using clustering-based multivariate density estimation algorithm

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6 Author(s)
Zhenya He ; Dept. of Radio Eng., Southeast Univ., Nanjing, China ; Luxi Yang ; Ju Liu ; Ziyi Lu
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A learning algorithm is developed for blind separation of the independent source signals from their linear mixtures. The algorithm is based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. We use a clustering-based multivariate density estimation approach to reduce the number of the parameters to be updated. Simulations illustrate the validity of the algorithm

Published in:

Signal Processing, IEEE Transactions on  (Volume:48 ,  Issue: 2 )

Date of Publication:

Feb 2000

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