Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Natural gradient learning for over- and under-complete bases in ICA
Neural Computation
Estimating Overcomplete Independent Component Bases for Image Windows
Journal of Mathematical Imaging and Vision
Entropy Optimization - Application to Blind Source Separation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Separating Convolutive Mixtures by Mutual Information Minimization
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive blind separation with an unknown number of sources
Neural Computation
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Learning Overcomplete Representations
Neural Computation
General approach to blind source separation
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Image Source Separation Using Color Channel Dependencies
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Novel nonGaussianity measure based BSS algorithm for dependent signals
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Music genre classification based on ensemble of signals produced by source separation methods
Intelligent Decision Technologies
Enhancement of source independence for blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Subband decomposition ICA (SDICA), an extension of ICA, assumes that each source is represented as the sum of some independent subcomponents and dependent subcomponents, which have different frequency bands. In this article, we first investigate the feasibility of separating the SDICA mixture in an adaptive manner. Second, we develop an adaptive method for SDICA, namely band-selective ICA (BS-ICA), which finds the mixing matrix and the estimate of the source independent subcomponents. This method is based on the minimization of the mutual information between outputs. Some practical issues are discussed. For better applicability, a scheme to avoid the high-dimensional score function difference is given. Third, we investigate one form of the overcomplete ICA problems with sources having specific frequency characteristics, which BS-ICA can also be used to solve. Experimental results illustrate the success of the proposed method for solving both SDICA and the overcomplete ICA problems.