基于二阶统计量的语音信号时域卷积盲分离算法  

A Time-Domain Blind Convolutive Separation Algorithm for Speech Signals Based on Second-Order Statistic

作　　者：张华[1] 冯大政[1] 庞继勇[2] 

机构地区：[1]西安电子科技大学雷达信号处理国家重点实验室,西安710071 [2]上海贝尔股份有限公司研究与创新中心,上海201206

出　　处：《电子与信息学报》2010年第5期1083-1087,共5页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60672128;60702057)资助课题

摘　　要：该文针对语音信号卷积混迭模型,利用信号间近似不相关和短时平稳等二阶统计特性,根据盲分离固有的排列和尺度不定性,改进最小二乘拟合函数,将关于混迭矩阵的四次函数转化为3组待定参数的二次函数。提出优化该代价函数的非正交联合块对角化算法,分3个子步,每个子步求解一个最小二乘问题,交替估计3组待定参数,逼近代价函数最小点。与ZJBD等同类方法相比,该方法具有计算复杂度低,估计精度高且对初始参数选择不敏感等特点,可直接在时域实现语音卷积盲分离。For blind convolutive separation of speech signals, a cost function improved from the least squares fitting function is developed based on second-order statistic, which simplifies the quartic-function with respect to the mixture matrix into three quadratic functions. The iternative approach with three sub-steps is proposed to perform the non-unitary joint block-diagonalization. In each sub-step, a closed solution is derived by minimizing the cost function associated with one parameter-group while fixing the others. Furthermore, the feature of low computational complexity is analytically proven. Compared with ZJBD, the simulations illustrate that the proposed algorithm has the merits of robust initialization selection and better estimate accuracy.

关 键 词：语音信号处理 卷积盲分离 非正交联合块对角化 二阶统计量 

分 类 号：TN912.3[电子电信—通信与信息系统]