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中文摘要：
为在时间域的 overdetermined convolutive 混合模型的一个盲目讲话来源分离方法基于 mutualindependence 和讲话 signals.Taking 的短时间的 stationarity 性质经由联合 block-diagonalization 被建议所有离开斜的亚矩阵的F标准的和作为一个标准，一个新奇联合 block-diagonalization 方法被建议通过最小化相应于混合 sub-matrices.Both 的二次的子函数的一个序列估计整个混合矩阵
英文摘要：
A blind speech source separation method for the overdetermined convolutive mixture model in time-domain is proposed via joint block-diagonalization based on the mutual- independence and short-time stationarity properties 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 method is proposed to estimate the whole mixture matrix through minimizing a sequence of quadratic sub-functions corresponding to mixture sub-matrices. Both theoretical analysis and simulations show that the proposed method has much lower complexity and faster convergence speed than the classical Jacobi-like method with no performance loss. In addition, there are almost no obvious impacts of the channel order and initialization values on the convergence speed.
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