论文标题
重新考虑语音分离网络中的分离层
Rethinking the Separation Layers in Speech Separation Networks
论文作者
论文摘要
所有现有语音分离网络中的模块都可以归类为单输入 - 多数输出（SIMO）模块和单输入单输出（SISO）模块。 SIMO模块生成的输出多于输入，SISO模块将输入数量和输出相同。尽管大多数分离模型仅包含SIMO架构，但还显示出与增强后SISO模块集成的某些两阶段分离系统可以提高分离质量。为什么通过合并SISO模块可以提高性能？ Simo模块总是必要的吗？在本文中，我们通过在SIMO和SISO模块中设计具有不同配置的模型来经验研究这些问题。我们表明，与仅使用Simo的标准设计相比，具有相同型号大小的混合SIMO-SISO设计能够改善分离性能，尤其是在低重叠条件下。我们进一步验证了SIMO模块的必要性，并表明仅SISO模型仍然能够在不牺牲性能的情况下进行分离。这些观察结果使我们能够重新考虑模型设计范式，并就如何执行分离的方式提出不同的看法。
Modules in all existing speech separation networks can be categorized into single-input-multi-output (SIMO) modules and single-input-single-output (SISO) modules. SIMO modules generate more outputs than input, and SISO modules keep the numbers of input and output the same. While the majority of separation models only contain SIMO architectures, it has also been shown that certain two-stage separation systems integrated with a post-enhancement SISO module can improve the separation quality. Why performance improvements can be achieved by incorporating the SISO modules? Are SIMO modules always necessary? In this paper, we empirically examine those questions by designing models with varying configurations in the SIMO and SISO modules. We show that comparing with the standard SIMO-only design, a mixed SIMO-SISO design with a same model size is able to improve the separation performance especially under low-overlap conditions. We further validate the necessity of SIMO modules and show that SISO-only models are still able to perform separation without sacrificing the performance. The observations allow us to rethink the model design paradigm and present different views on how the separation is performed.
