卷积混迭语音信号的联合块对角化盲分离方法

针对语音信号的卷积混迭模型,利用不同语音信号之间的近似独立和短时平稳特性,提出一种基于信号二阶统计量的联合块对角化方法,解决超定卷积盲分离问题。该方法采用非对角线上各子矩阵 F -范数的平方和作为联合块对角化性能的评判准则,将原四次代价函数转化为一组较为简单的二次子代价函数,每一子代价函数用于估计酉混迭矩阵的一个子矩阵。依次最小化各子函数,迭代搜索代价函数最小点,得到混迭矩阵的估计。理论分析及实验结果表明,所提方法不仅能够达到与类Jacobi经典方法同样好的分离效果,并且具有更低的计算复杂度、更快的收敛速度和对传输信道阶数、迭代初始值不敏感的特点。 

Blind convolutive separation algorithm for speech signals via joint block diagonalization

A blind speech source separation algorithm for the overdetermined convolutive mixture model in time-domain is proposed via joint block-diagonalization based on the mutual-independence property and the short-time stationary of the speech signals.Taking the sum of the F -norms of all off-diagonal sub-matrices as a criterion,a novel joint block-diagonalization algorithm is proposed to estimate the whole mixture matrix through minimizing a sequence of quadratic subfunctions corresponding to mixture submatrices.Both theoretical analysis and simulation results show that the proposed algorithm has much lower complexity and faster convergence speed than the classical Jacobi-like method with no performance loss.In addition,there almost are no obvious impacts of the channel order and initialization values on the convergence speed.