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A Local Dominance Based Single Source Points Detector for Mixing Matrix Estimation

机译：基于局部优势的单源点检测器，用于混合矩阵估计

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摘要

In this paper, a single source points (SSPs) detection method based on local dominance is devised for mixing matrix estimation in underdetermined blind source separation (UBSS). In the proposed detector, time-frequency (TF) points of mixed signals are firstly divided into different groups, and the local covariance matrix of each group is calculated. Taking advantage of the local dominance property, the groups with rank-one local covariance matrices are then identified as single source groups, i.e., the TF points of mixed signals in single source groups are regarded as SSPs. Finally, the obtained SSPs are clustered by the hierarchical clustering algorithm to achieve mixing matrix estimation. Simulations with real audio sources show that the proposed method yields competitive robustness, efficiency and effectiveness, in comparison with the traditional methods.
机译：在本文中，设计了基于局部优势的单个源点（SSP）检测方法，用于混合未确定的盲源分离（UBS）中的矩阵估计。 在所提出的检测器中，将混合信号的时频（TF）点首先划分为不同的组，并且计算每个组的局部协方差矩阵。 利用本地优势特性，然后将具有级别一个局部协方差矩阵的组被识别为单源组，即单个源组中的混合信号的TF点被视为SSP。 最后，通过分层聚类算法聚集所获得的SSP以实现混合矩阵估计。 与真正音频来源的模拟表明，与传统方法相比，该方法产生了竞争的鲁棒性，效率和有效性。

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