论文标题
关于神经源分离系统的深面膜估计模块
On the Use of Deep Mask Estimation Module for Neural Source Separation Systems
论文作者
论文摘要
最近的大多数神经源分离系统都依赖于基于掩蔽的管道，其中一组乘法掩模是根据输入混合物的信号表示估算并应用于输入混合物的信号表示的。在几乎所有网络体系结构中，此类掩码的估计是由单层完成的，然后是可选的非线性激活函数。但是，与浅面膜估计模块相比，最近的文献研究了使用深面膜估计模块的使用和观察到的性能提高。在本文中，我们通过将其连接到最近提出的无监督的源分离方法来分析这种更深层面的掩码估计模块的作用，并从经验上表明，深面膜估计模块是与所谓的超级分离组的有效近似，该模块具有常规的浅层面罩估计层。
Most of the recent neural source separation systems rely on a masking-based pipeline where a set of multiplicative masks are estimated from and applied to a signal representation of the input mixture. The estimation of such masks, in almost all network architectures, is done by a single layer followed by an optional nonlinear activation function. However, recent literatures have investigated the use of a deep mask estimation module and observed performance improvement compared to a shallow mask estimation module. In this paper, we analyze the role of such deeper mask estimation module by connecting it to a recently proposed unsupervised source separation method, and empirically show that the deep mask estimation module is an efficient approximation of the so-called overseparation-grouping paradigm with the conventional shallow mask estimation layers.
