Title :
Blind separation of instantaneous mixture of sources based on order statistics
Author_Institution :
Lab. of Modeling & Computation, CNRS, Grenoble, France
fDate :
2/1/2000 12:00:00 AM
Abstract :
In this paper, we introduce a novel procedure for separating an instantaneous mixture of sources based on order statistics. The method is derived in a general context of independence component analysis, using a contrast function defined in term of the Kullback-Leibler divergence or of the mutual information. We introduce a discretized form of this contrast permitting its easy estimation through order statistics. We show that the local contrast property is preserved and derive a global contrast, exploiting only the information of the support of the distribution (in case this support is finite). Some simulations are given, illustrating the good performance of the method
Keywords :
signal processing; statistical analysis; Kullback-Leibler divergence; blind separation; contrast function; estimation; global contrast; independence component analysis; instantaneous mixture; local contrast property; mutual information; order statistics; Computational modeling; Entropy; Independent component analysis; Information analysis; Mutual information; Probability distribution; Statistical analysis; Statistical distributions; Statistics; Vectors;
Journal_Title :
Signal Processing, IEEE Transactions on