Blind partial separation of instantaneous mixtures of sources

Authors:
Dinh-Tuan Antoine Pham
Affiliations:
Laboratoire de Modélisation et Calcul, Grenoble, France
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 4
Cited 2

Elements of information theory

Elements of information theory
Fast algorithms for mutual information based independent component analysis

IEEE Transactions on Signal Processing - Part I
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach

IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

Blind separation of sources that have spatiotemporal variance dependencies

Signal Processing - Special issue on independent components analysis and beyond
Minimum range approach to blind partial simultaneous separation of bounded sources: Contrast and discriminacy properties

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a general criterion for blindly extracting a subset of sources in instantaneous mixtures. We derive the corresponding estimation equations and generalize them based on arbitrary nonlinear separating functions. A quasi-Newton algorithm for minimizing the criterion is presented, which reduces to the FastICA algorithm in the case when only one source is extracted. The asymptotic distribution of the estimator is obtained and a simulation example is provided.