APSO算法在语音信号盲源分离中的研究

Study on adaptive particle swarm optimization algorithm in speech signal blind source separation

将自适应粒子群优化(Adaptive Praticle Swarm Optimization)算法运用到语音信号的盲源分离中,以峰度为目标函数,通过自适应调整惯性因子,克服了收敛速度和分离效果之间的矛盾,最终实现盲源分离。该算法避免了传统的优化算法自然梯度法稳定性差、易于陷入局部最优的不足。通过比较仿真结果和性能指标可以看出,APSO算法提高了收敛速度,改善了分离性能,表明了该算法在实现语音信号盲源分离中性能的优越性。

This paper introduces adaptive particle swarm optimization algorithm to speech signal blind source separation, with the kurtosis as target function. Adaptive PSO algorithm overcomes the conflict between convergence speed and separation efficiency, and finally realizes the blind source separation by adjusting the inertia factor. The algorithm avoids the flaws of poor stability and inclination to get into the local superior characterizing the traditional natural gradient method. Comparison of the simulation results and performance indicators shows that adaptive particle swarm optimization method improves the convergence speed and sepa- ration properties, indicating the superiority of the performance of PSO algorithm in realizing blind source separation of speech signal.