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Source Number Estimation and Effective Channel Order Determination Based on Higher-Order Tensors

机译：基于高阶张量的信源数估计和有效信道顺序确定

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摘要

Source number estimation is an essential task in underdetermined convolutive blind source separation, and effective channel order determination is also a challenging issue. For solving the two problems, the classical methods are based on information theoretic criteria. However, these are prone to the underestimation and overestimation of the number of sources in the underdetermined case. To compensate for this shortcoming, in this paper we propose two algorithms based on higher-order tensors. First, an improved algorithm is presented to estimate the number of sources. By transforming the tensor into a matrix, the eigenvalues of the resultant matrices are used to estimate the number of sources. Additionally, we employ higher-order tensors to detect the effective channel order and confirm the relationship between the number of sources and the effective channel order in the convolutive mixture model. Finally, a series of simulation experiments demonstrate that the proposed algorithms have an advantage over the conventional methods.
机译：在不确定的卷积盲源分离中，源数量估计是必不可少的任务，有效的信道顺序确定也是一个具有挑战性的问题。为了解决这两个问题，经典方法基于信息理论标准。但是，在不确定的情况下，这些方法容易低估和高估来源的数量。为了弥补这一缺点，本文提出了两种基于高阶张量的算法。首先，提出了一种改进的算法来估计源数量。通过将张量转换为矩阵，所得矩阵的特征值可用于估计源的数量。此外，我们使用高阶张量来检测有效通道顺序，并在卷积混合模型中确认源数量与有效通道顺序之间的关系。最后，一系列仿真实验表明，所提出的算法具有优于传统方法的优势。

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