论文标题
在回响和嘈杂的设置下未知数量的扬声器的单频道语音分离
Single channel voice separation for unknown number of speakers under reverberant and noisy settings
论文作者
论文摘要
我们提出了一个统一的网络，用于语音分离未知数量的扬声器。所提出的方法由与扬声器分类分支一起优化的几个分离头组成。分离是在时域中进行的，以及所有分离头之间的参数共享。分类部门估计说话者的数量，而每个头则专门用于分开不同数量的扬声器。我们在干净和嘈杂的混响设置下评估了所提出的模型。结果表明，所提出的方法优于基线模型。此外，我们提供了一个新的嘈杂和回响的数据集，该数据集最多可同时讲话。
We present a unified network for voice separation of an unknown number of speakers. The proposed approach is composed of several separation heads optimized together with a speaker classification branch. The separation is carried out in the time domain, together with parameter sharing between all separation heads. The classification branch estimates the number of speakers while each head is specialized in separating a different number of speakers. We evaluate the proposed model under both clean and noisy reverberant set-tings. Results suggest that the proposed approach is superior to the baseline model by a significant margin. Additionally, we present a new noisy and reverberant dataset of up to five different speakers speaking simultaneously.
