文档介绍：电子测量技术
Electronic Measurement Technology
ISSN 1002-7300,CN 11-2175/TN
卷积神经网络；自适应特征融合；VGG19；直方图均衡化增强
中图分类号： 文献标志码：A 国家标准学科分类代码：
Speech enhancement based on UNet adaptive feature fusion
REN Jian LI Hongyan ZHANG Yu XING Lu
(College of Information and Computer Science, Taiyuan University of Technology, Yuci 030600, China)
Abstract: Aiming at the problem that the traditional speech enhancement network is not ideal for unknown noise enhancement,
this paper proposes an improved method from the aspects of spectral enhancement, network structure and feature fusion mechanism.
Firstly, in order to extract the deep feature information of the spectrum, VGG19 structure was used to replace the encoder part of
UNet structure, and residual network was added to the decoder part to deepen the network depth and prevent the training degradation.
Secondly, in order to better combine the feature information in the spectrogram, an adaptive feature fusion mechanism is added to the
jump connection part of THE UNet structure to fuse the deep and shallow features. In addition, in order to enhance the speaker in-
formation,