@inproceedings{7c973a184ab5414c8466874795cc9967,
title = "Residual Unet with Attention Mechanism for Time-Frequency Domain Speech Enhancement",
abstract = "Eliminating the negative effects of background environmental noise is an interesting and challenging task in audio processing. In recent years, denoising technology based on neural networks (NN) has achieved good performance. In particular, the structure based on the convolutional encoder and decoder has been proven to achieve good enhancement effects. On this basis, this paper proposes a residual unet structure combined with the attention mechanism. Effectively reduce the impact of gradient disappearance on network training, and improve the semantic gap between encoder output and decoder output due to unet shortcut connections. The experimental results show that compared with the DNN baseline and unet network, the enhanced voice quality has been significantly improved.",
keywords = "Speech enhancement, Unet, attention gating, residual unit",
author = "Hanyu Chen and Xiwei Peng and Qiqi Jiang and Yujie Guo",
note = "Publisher Copyright: {\textcopyright} 2022 Technical Committee on Control Theory, Chinese Association of Automation.; 41st Chinese Control Conference, CCC 2022 ; Conference date: 25-07-2022 Through 27-07-2022",
year = "2022",
doi = "10.23919/CCC55666.2022.9902215",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7007--7011",
editor = "Zhijun Li and Jian Sun",
booktitle = "Proceedings of the 41st Chinese Control Conference, CCC 2022",
address = "United States",
}