This study investigates mask-based beamformers (BFs), which estimate filters for target sound extraction (TSE) using time-frequency masks. Although multiple mask-based BFs have been proposed, no consensus has been reached on which one offers the best target-extraction performance. Previously, we found that maximum signal-to-noise ratio and minimum mean square error (MSE) BFs can achieve the same extraction performance as the theoretical upper-bound performance, with each BF containing a different optimal mask. However, two issues remained unsolved: only two BFs were covered, excluding the minimum variance distortionless response BF, and ideal scaling (IS) was employed to ideally adjust the output scale, which is not applicable to realistic scenarios. To address these issues, this study proposes a unified framework for mask-based BFs comprising two processes: filter estimation that can cover all possible BFs and scaling applicable to realistic scenarios by employing a mask to generate a scaling reference. Based on the operators and covariance matrices used in BF formulas, all possible BFs can be classified into 12 variations, including two new ones. Optimal masks for both processes are obtained by minimizing the MSE between the target and BF output. The experimental results using the CHiME-4 dataset suggested that 1) all 12 variations can achieve the theoretical upper-bound performance, and 2) mask-based scaling can behave like IS, even when constraining the temporal mean of a non-negative mask to one. These results can be explained by considering the practical parameter count of the masks. These findings contribute to 1) designing a TSE system, 2) improving scaling accuracy through mask-based scaling, and 3) estimating the extraction performance of a BF.

翻译：本研究探讨了基于掩码的波束形成器，该技术利用时频掩码来估计目标声音提取的滤波器。尽管已提出多种基于掩码的波束形成器，但关于哪种方法能提供最佳目标提取性能尚未达成共识。先前我们发现，最大信噪比和最小均方误差波束形成器可以实现与理论上限性能相同的提取效果，且每种波束形成器包含不同的最优掩码。然而，仍有两大问题未解决：仅涵盖两种波束形成器（未包含最小方差无失真响应波束形成器），且采用了理想缩放来调整输出尺度，这在现实场景中并不适用。为解决这些问题，本研究提出了一个统一的基于掩码波束形成器框架，包含两个过程：可覆盖所有可能波束形成器的滤波器估计，以及通过使用掩码生成缩放参考以适应现实场景的缩放方法。根据波束形成器公式中使用的算子和协方差矩阵，所有可能的波束形成器可分为12种变体，其中包括两种新变体。通过最小化目标信号与波束形成器输出之间的均方误差，可获得两个过程的最优掩码。使用CHiME-4数据集的实验结果表明：1）所有12种变体均能达到理论上限性能；2）即使将非负掩码的时间均值约束为1，基于掩码的缩放仍能表现出与理想缩放相似的效果。这些结果可通过考虑掩码的实际参数数量来解释。本研究的发现有助于：1）设计目标声音提取系统；2）通过基于掩码的缩放提高缩放精度；3）评估波束形成器的提取性能。