Blind dependent sources separation method using wavelet

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.

[1]  Michael Zibulevsky,et al.  Underdetermined blind source separation using sparse representations , 2001, Signal Process..

[2]  Andrzej Cichocki,et al.  Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation , 2000, Neural Computation.

[3]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[4]  Yannick Deville,et al.  A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources , 2005, Signal Process..

[5]  Andrzej Cichocki,et al.  Robust Blind Source Separation Utilizing Second and Fourth Order Statistics , 2002, ICANN.

[6]  Bao Zheng,et al.  Grading learning for blind source separation , 2003 .

[7]  Yan Wang,et al.  Decision support under uncertainties based on robust Bayesian networks in reverse logistics management , 2009, Int. J. Comput. Appl. Technol..

[8]  Y. Deville,et al.  Time–frequency ratio-based blind separation methods for attenuated and time-delayed sources , 2005 .

[9]  保铮,et al.  Grading learning for blind source separation , 2003 .

[10]  Moeness G. Amin,et al.  Blind source separation based on time-frequency signal representations , 1998, IEEE Trans. Signal Process..

[11]  Marie-Françoise Lucas,et al.  Optimized Wavelets for Blind Separation of Nonstationary Surface Myoelectric Signals , 2008, IEEE Transactions on Biomedical Engineering.

[12]  Terrence J. Sejnowski,et al.  Blind source separation of more sources than mixtures using overcomplete representations , 1999, IEEE Signal Processing Letters.

[13]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[14]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[15]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[16]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[17]  Jiing-Yih Lai,et al.  B-spline surface fitting to random points with bounded boundary conditions , 2007, Int. J. Comput. Appl. Technol..

[18]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[19]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[20]  Aapo Hyvärinen,et al.  Blind source separation by nonstationarity of variance: a cumulant-based approach , 2001, IEEE Trans. Neural Networks.

[21]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.