Elements of information theory
Elements of information theory
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Time series: data analysis and theory
Time series: data analysis and theory
Advanced ICA-based receivers for block fading DS-CDMA channels
Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation
Neural Computation
Dependence, correlation and Gaussianity in independent component analysis
The Journal of Machine Learning Research
A multiscale framework for blind separation of linearly mixed signals
The Journal of Machine Learning Research
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Subband-Based Blind Separation for Convolutive Mixtures of Speech
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Signal Processing - Special issue: Information theoretic signal processing
Separation of statistically dependent sources using an L2-distance non-Gaussianity measure
Signal Processing - Special section: Distributed source coding
Fast kernel density independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Enhancement of source independence for blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Self-adaptive blind source separation based on activation functions adaptation
IEEE Transactions on Neural Networks
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Adaptive 2-D wavelet transform based on the lifting scheme with preserved vanishing moments
IEEE Transactions on Image Processing
Music genre classification based on ensemble of signals produced by source separation methods
Intelligent Decision Technologies
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Sub-band decomposition independent component analysis (SDICA) assumes that wide-band source signals can be dependent but some of their sub-components are independent. Thus, it extends applicability of standard independent component analysis (ICA) through the relaxation of the independence assumption. In this paper, firstly, we introduce novel wavelet packets (WPs) based approach to SDICA obtaining adaptive sub-band decomposition of the wideband signals. Secondly, we introduce small cumulant based approximation of the mutual information (MI) as a criterion for the selection of the sub-band with the least-dependent components. Although MI is estimated for measured signals only, we have provided a proof that shows that index of the sub-band with least dependent components of the measured signals will correspond with the index of the sub-band with least dependent components of the sources. Unlike in the case of the competing methods, we demonstrate consistent performance in terms of accuracy and robustness as well as computational efficiency of WP SDICA algorithm.