Hand-crafted spatial features (e.g., inter-channel phase difference, IPD)
play a fundamental role in recent deep learning based multi-channel speech
separation (MCSS) methods. However, these manually designed spatial features
are hard to incorporate into the end-to-end optimized MCSS framework. In this
work, we propose an integrated architecture for learning spatial features
directly from the multi-channel speech waveforms within an end-to-end speech
separation framework. In this architecture, time-domain filters spanning signal
channels are trained to perform adaptive spatial filtering. These filters are
implemented by a 2d convolution (conv2d) layer and their parameters are
optimized using a speech separation objective function in a purely data-driven
fashion. Furthermore, inspired by the IPD formulation, we design a conv2d
kernel to compute the inter-channel convolution differences (ICDs), which are
expected to provide the spatial cues that help to distinguish the directional
sources. Evaluation results on simulated multi-channel reverberant WSJ0 2-mix
dataset demonstrate that our proposed ICD based MCSS model improves the overall
signal-to-distortion ratio by 10.4% over the IPD based MCSS model.

本研究提出了一种基于 2d 卷积层的端到端架构，通过训练时域滤波器来学习从多通道语音波形中提取空间特征，使用互通卷积差分技术 (ICD) 提高多通道语音分离模型的分离性能。

通过空间特征学习增强端到端多通道语音分离

Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature  Learning

The end-to-end approach for single-channel speech separation has been studied
recently and shown promising results. This paper extended the previous approach
and proposed a new end-to-end model for multi-channel speech separation. The
primary contributions of this work include 1) an integrated waveform-in
waveform-out separation system in a single neural network architecture. 2) We
reformulate the traditional short time Fourier transform (STFT) and
inter-channel phase difference (IPD) as a function of time-domain convolution
with a special kernel. 3) We further relaxed those fixed kernels to be
learnable, so that the entire architecture becomes purely data-driven and can
be trained from end-to-end. We demonstrate on the WSJ0 far-field speech
separation task that, with the benefit of learnable spatial features, our
proposed end-to-end multi-channel model significantly improved the performance
of previous end-to-end single-channel method and traditional multi-channel
methods.

本文提出了一种基于端到端的多通道语音分离模型，通过可学习空间特征的提出，在 WSJ0 远场语音分离任务中显著改进了单通道和传统多通道方法的性能。