改进粒子群算法在水下盲语音分离中的应用研究

doi:10.3969/j.issn.0255-8297.2018.04.003

Abstract

Abstract: A new independent component analysis (ICA) algorithm optimized from the improved particle swarm optimization (PSO) is proposed to overcome the drawbacks of the slow convergence speed and the aptness into local minimum of the PSO algorithm. The proposed method is aimed at extracting the target speech signal in the under-water noisy environment. It uses the absolute value of normalized fourth-order cumulant as an objective function. By changing the inertia factor ω and constriction factor k, particles have more adaptive ability to find out the optimal particle quickly. Comparing with the classical PSO algorithm, the proposed improved method performs faster convergence speed, better algorithm stability and superior separation effect.

Key words: blind speech separation, fourth-order cumulant, particle swarm optimization (PSO), under-water speech communication, independent component analysis (ICA)

CLC Number:

TP391

WANG Guang-yan, GENG Yan-xiang, CHEN Lei. Application Research of Improved Particle Swarm Algorithm in Underwater Speech Blind Separation[J]. Journal of Applied Sciences, 2018, 36(4): 589-600.

References

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Application Research of Improved Particle Swarm Algorithm in Underwater Speech Blind Separation

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