학술논문
Improve speech enhancement with Wave-USE-Net
Document Type
Conference
Author
Source
2023 4th International Conference on Information Science and Education (ICISE-IE) Information Science and Education (ICISE-IE), 2023 4th International Conference on. :225-228 Dec, 2023
Subject
Language
Abstract
At present, most deep learning speech enhancement models rely on spectrograms, and phase estimation is needed to restore the speech signal from spectrograms. In this paper, a speech enhancement model called Wave-USE-Net is proposed, which combines Squeeze-and-Excitation Networks(SENet) and Wave-U-Net. On the basis of Wave-U-Net directly estimating the waveform of the enhanced speech signal end-to-end, the SE block is used to suppress redundant features and effectively recover the enhanced speech signal. The experimental results show that compared with the baseline Wave-U-Net model, the proposed Wave-USE-Net model improves PESQ, CSIG, CBAK, COVL and SSNR by 7.5%,7.1%,1.2%,8.5% and 1.1% respectively on the VCTK dataset. The enhancement effect of the proposed model is better than that of other speech enhancement models based on Wave-U-Net, and the enhanced speech signal is closer to clean speech.