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
High-order contrasts for independent component analysis
Neural Computation
Robust Blind Source Separation Utilizing Second and Fourth Order Statistics
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
B-spline surface fitting to random points with bounded boundary conditions
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
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The purpose of this paper is to develop novel Blind Source Separation (BSS) algorithm from linear mixtures of dependent sources signals. Most of the proposed algorithms for solving BSS problem rely on independence or at least non-correlation assumption of source signals. However, in practice, the latent sources are usually dependent to some extent. On the other hand, there is a large variety of applications that require considering sources that usually behave light or strong dependence. The proposed algorithm is developed based on the wavelet coefficient representations using Continuous Wavelet Transformation (CWT) which only requires slight differences in the CWT coefficient of the considered signals in the same scale. Moreover, the proposed algorithm can extract the desired signals in the overcomplete conditions. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.