Introducing an FPGA based genetic algorithms in the applications of blind signals separation

Genetic Algorithms (GAs) are one of the most advanced optimization techniques. The main objective of this paper, is introducing an FPGA implementation based genetic algorithm, then applying it, as an adaptive algorithm, on a nonlinear adaptive filters for the purpose of blind signals separation. In this case, the nonlinear estimator has been used to predict the error filter and GA will be used to optimize the filter coefficients through the search for a near optimum solution. The proposed Hardware Genetic Algorithms (HGA) has been presented and tested, first, by different sine wave signals, then by audio wave signals to judge the design separation capability. The implementation results declare that HGA approach significantly enhances the system performance as a step toward real time performance.

[1]  Paris Smaragdis,et al.  Efficient blind separation of convolved sound mixtures , 1997, Proceedings of 1997 Workshop on Applications of Signal Processing to Audio and Acoustics.

[2]  Te-Won Lee,et al.  Blind Separation of Delayed and Convolved Sources , 1996, NIPS.

[3]  Shun-ichi Amari,et al.  Sequential blind signal extraction in order specified by stochastic properties , 1997 .

[4]  A. Mansour,et al.  Subspace adaptive algorithm for blind separation of convolutive mixtures by conjugate gradient method , 1998 .

[5]  Reinhold Orglmeister,et al.  Blind source separation of real world signals , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[6]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[7]  Philippe Loubaton,et al.  Subspace method for blind separation of sources in convolutive mixture , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[8]  Kari Torkkola,et al.  Blind separation of convolved sources based on information maximization , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

[9]  Sam Kwong,et al.  Genetic Algorithms : Concepts and Designs , 1998 .

[10]  Kari Torkkola,et al.  Blind separation of delayed sources based on information maximization , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[11]  Paris Smaragdis,et al.  Evaluation of blind signal separation methods , 1999 .

[12]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[13]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  A. Nandi.  Blind estimation using higher-order statistics , 1999 .

[16]  Terrence J. Sejnowski,et al.  A Non-linear Information Maximisation Algorithm that Performs Blind Separation , 1994, NIPS.