Blind Source Separation Research Based on the Feature Distance Using Evolutionary Algorithms

Without any information on the mixing system, the blind source separation (BSS) technique efficiently separates mixed signals. The approach called evolutionary algorithms was used for the BSS problem in this paper. The fitness function based on the feature distance and kurtosis was proposed to measure the degree of the separated signals in this paper. Compared with the traditional algorithm in the BSS problem, the mathematical calculation and the physical significance of the separated signals are both taken into consideration in the proposed method. Therefore, the separated signals could have great correlation with the original individual signal and could be used in the additional signal processing step with good signal property. Experimental results on mixed spoken signals indicated that the established evolutionary algorithm of particle swarm optimization (PSO) and genetic algorithm (GA) could effectively solve the BSS problem from the signal feature distance and independence measurement. The study in this paper was implemented with MATLAB language.