论文标题
基于学生t分布
Convergence-guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's t Distribution
论文作者
论文摘要
在本文中，我们解决了一个盲目的分离（BSS）问题，并提出了一个独立阳性半量张量分析（IPSDTA）的新扩展框架。 IPSDTA是一种最先进的BSS方法，使我们能够考虑频率相关性，但是生成模型在多元高斯分布中受到限制，其参数优化算法不能保证稳定的收敛性。首先，为了解决这些问题，我们建议将生成模型扩展到可以处理各种信号的参数多元学生的t分布。其次，我们得出了一种新的参数优化算法，该算法保证成本函数中的单调非渗透率，从而提供稳定的收敛性。实验结果表明，常规IPSDTA中的成本函数并未显示单调的非信息性能。另一方面，所提出的方法保证了成本函数中的单调非提示，并在源分离性能中胜过常规的ILRMA和IPSDTA。
In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a state-of-the-art BSS method that enables us to take interfrequency correlations into account, but the generative model is limited within the multivariate Gaussian distribution and its parameter optimization algorithm does not guarantee stable convergence. To resolve these problems, first, we propose to extend the generative model to a parametric multivariate Student's t distribution that can deal with various types of signal. Secondly, we derive a new parameter optimization algorithm that guarantees the monotonic nonincrease in the cost function, providing stable convergence. Experimental results reveal that the cost function in the conventional IPSDTA does not display monotonically nonincreasing properties. On the other hand, the proposed method guarantees the monotonic nonincrease in the cost function and outperforms the conventional ILRMA and IPSDTA in the source-separation performance.
