Blind source separation using clustering-based multivariate densityestimation algorithm

Authors:
Zhenya He;Luxi Yang;Ju Liu;Ziyi Lu;Chen He;Yuhui Shi
Affiliations:
Dept. of Radio Eng., Southeast Univ., Nanjing;-;-;-;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2000

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Abstract

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