论文标题
基于ILRMA的联合 - 分节化过程的基于ILRMA的先验分布
Regularized Fast Multichannel Nonnegative Matrix Factorization with ILRMA-based Prior Distribution of Joint-Diagonalization Process
论文作者
论文摘要
在本文中，我们通过引入先前的信息，以实现空间协方差矩阵模型的联合对角线化，提出了FastMNMF的新扩展框架，并提出了一个新的扩展框架。最近，在假设多个来源的空间协方差矩阵可以被共同化为对角线的假设下，已提出了FastMNMF作为多通道非负矩阵分解的快速版本。但是，其源分离性能没有得到改善，联合 - 分节化过程的物理含义尚不清楚。为了解决这些问题，我们首先揭示了独立低级矩阵分析（ILRMA）中使用的联合 - 分节化过程与混合系统之间的密切关系。接下来，在这一事实的激励下，我们提出了一个由ILRMA支持的新正规FastMNMF，并得出了收敛保证的参数更新规则。从BSS实验中，我们显示所提出的方法以几乎相同的计算时间优于源分离精度的常规FASTMNF。
In this paper, we address a convolutive blind source separation (BSS) problem and propose a new extended framework of FastMNMF by introducing prior information for joint diagonalization of the spatial covariance matrix model. Recently, FastMNMF has been proposed as a fast version of multichannel nonnegative matrix factorization under the assumption that the spatial covariance matrices of multiple sources can be jointly diagonalized. However, its source-separation performance was not improved and the physical meaning of the joint-diagonalization process was unclear. To resolve these problems, we first reveal a close relationship between the joint-diagonalization process and the demixing system used in independent low-rank matrix analysis (ILRMA). Next, motivated by this fact, we propose a new regularized FastMNMF supported by ILRMA and derive convergence-guaranteed parameter update rules. From BSS experiments, we show that the proposed method outperforms the conventional FastMNMF in source-separation accuracy with almost the same computation time.
