Time-domain single-channel speech enhancement (SE) still remains challenging
to extract the target speaker without any prior information on multi-talker
conditions. It has been shown via auditory attention decoding that the brain
activity of the listener contains the auditory information of the attended
speaker. In this paper, we thus propose a novel time-domain brain-assisted SE
network (BASEN) incorporating electroencephalography (EEG) signals recorded
from the listener for extracting the target speaker from monaural speech
mixtures. The proposed BASEN is based on the fully-convolutional time-domain
audio separation network. In order to fully leverage the complementary
information contained in the EEG signals, we further propose a convolutional
multi-layer cross attention module to fuse the dual-branch features.
Experimental results on a public dataset show that the proposed model
outperforms the state-of-the-art method in several evaluation metrics. The
reproducible code is available at this https URL

本研究提出了一种基于脑电图信号的时域单通道语音增强网络（BASEN），用于从混响环境中提取目标讲话者的语音，并且实验结果显示，该方法在多项评估指标上表现优于现有方法。