APPROACHES FOR BLIND SEPARATION OF SOURCES BASED ON MULTIVARIATE DENSITY ESTIMATION
Zhenya He, Lüxi Yang, Ju Liu, Ziyi Lu, Chen He, Yuhui Shi · Journal of Circuits Systems and Computers · 1999
A class of learning algorithms is developed for the blind separation of independent source signals from their linear mixtures. The algorithms are based on the Kullback–Leibler distance. A multivariate density estimation technique is used in estimating the probability density function of independent components. Simulations using speech signals and images as sources illustrate the performances of the algorithms.