基于新型混合模型的欠定盲分离方法  

A Method for Under-determined Blind Source Separation Based on New Mixture Model

作　　者：陈永强[1,2] 王宏霞[1] 

机构地区：[1]西南交通大学信息科学与技术学院,成都610031 [2]成都信息工程学院电子实验中心,成都610225

出　　处：《自动化学报》2014年第7期1412-1420,共9页Acta Automatica Sinica

基　　金：国家自然科学基金(61170226);中央高校基本科研业务费专项资金(SWJTU11CX047;SWJTU12ZT02);四川省青年科技创新研究团队项目(2011JTD0007)资助~~

摘　　要：针对欠定盲分离问题,提出了一种新的源恢复方法.在时频域局部区域采用复高斯分布对源信号进行建模,将语音信号的稀疏性和局部平稳性结合在一起,提出了一种新的混合模型来描述观测信号在局部区域的概率分布.通过该模型,将每个时频点的源信号状态的判断问题转换成模型的参数估计和后验概率的计算问题,最后通过子混合矩阵的逆恢复出源信号.实验结果表明,该方法具有很快的收敛速度,并且比已有方法具有更好的分离性能.To solve the problem of under-determined blind source separation, we propose a new source recovery method. By utilizing the complex valued Gaussian model to characterize the local distribution of source signals in each micro-region in the time-frequency domain and combining speech signalsr sparsity with their local stability, a new mixture model is derived to characterize the local distribution of observed signals. We convert the problem of judging the state of each source signal at each time-frequency point into a problem of model＇s parameters estimation and posterior probability computation. Finally, the source signals are recovered by sub-mixing matrixes inverse. Experiment results show that the proposed method converges very fast and has better separation performance compared with the existing methods.

关 键 词：欠定盲分离 混合模型 稀疏性 局部复高斯分布 最大后验概率 

分 类 号：TN911.7[电子电信—通信与信息系统]