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Blind separation of mixture of independent sources through a quasi-maximum likelihood approach

机译:通过拟最大似然方法盲分离独立来源的混合物

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We propose two methods for separating mixture of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood (ML) solution corresponding to some given distributions of the sources and relaxing this assumption afterward. The first method is specially adapted to temporally independent non-Gaussian sources and is based on the use of nonlinear separating functions. The second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters. A theoretical analysis of the performance of the methods has been made. A simple procedure for optimally choosing the separating functions is proposed. Further, in the second method, a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided. Finally, some numerical and simulation results are given, illustrating the performance of the method and the good agreement between the experiments and the theory.
机译:我们提出了两种方法来分离独立来源的混合,而无需对它们的概率分布有任何确切的了解。通过考虑对应于某些给定源分布的最大似然(ML)解并随后放宽此假设来获得它们。第一种方法特别适合于时间独立的非高斯源,并且基于非线性分离函数的使用。第二种方法专门适用于具有不同光谱的相关光源,并且基于线性分离滤波器的使用。对方法的性能进行了理论分析。提出了一种最佳选择分离函数的简单方法。此外,在第二种方法中,提供了基于两个对称矩阵的同时对角化的简单实现。最后,给出了一些数值和仿真结果,说明了该方法的性能以及实验与理论之间的良好一致性。

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