Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Flexible Independent Component Analysis
Journal of VLSI Signal Processing Systems
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Energy, entropy and information potential for neural computation
Energy, entropy and information potential for neural computation
Blind wideband source separation
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Multichannel signal separation: methods and analysis
IEEE Transactions on Signal Processing
Signal separation by symmetric adaptive decorrelation: stability,convergence, and uniqueness
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
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In this paper, a novel technique for blind signal separation based on a combination of second order and higher order approaches is introduced. The problem of blind signal separation was solved in a wavelet domain.. The main idea behind this approach is that the mixing signal can be decomposed into a sum of uncorrelated and/ or independent sub-bands using the wavelet transform. In the beginning, the observed signal is prewhitened in the time domain then, the initial separation matrix will be estimated from second order statistics decorrelation method in the wavelet domain. The estimating matrix will be used as an initial separating matrix in the higher order statistics method in order to estimate the final separation matrix. The algorithm was tested using natural images. Extensive experiments have confirmed that the use of the proposed procedure provides promising result in separating the image from noisy mixtures of images.