摘要
针对信道对声音信号的卷积效应,提出了一种基于粒子群优化的盲解卷积新算法。利用信号的时间可预测性作为盲解卷积的解卷测度,采用粒子群优化算法对基于该测度的代价函数进行优化求解,从而成功得到解卷积滤波器系数,实现对信号的盲解卷积。仿真实验表明,所提出的算法对于声音信号具有很好的盲解卷积效果,所恢复信号与原始信号的相关系数和重构信噪比均较高。
This paper proposed a novel blind deconvolution algorithm based on PSO to remove the convolution effect for sound signals in communication channel. It used the temporal predictability as the measure of deconvolution and used PSO for optimi- zing the cost function based on it. Then, the filter coefficient for deconvolution could be got and the signals could be deconvo- luted successfully. Simulation results show that the deconvolution property of the algorithm for sound signals is good. The val- ues of correlation coefficient and signal-to-noise ratio between recovered signals and source signals are all high.
出处
《计算机应用研究》
CSCD
北大核心
2014年第4期1001-1004,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(11127202)
关键词
盲解卷积
时间可预测性
粒子群优化
声音信号
blind deconvolution
temporal predictability
particle swarm optimization（PSO）
sound signal0
