Abstract: In order to overcome the adverse effects of environmental and channel noise on speech communication quality in radio systems and improve the speech quality of radio communication,this paper proposes a deep separable network called CLU-Net(channel attention and LSTM-based U-Net),which adopts the deep U-shape architecture and long short-term memory(LSTM).In the network,deep separable convolution is used to implement low-complexity feature coding.The combination of attention mechanisms and LSTM can pay attention to the relationship between different convolution channels and the context of clean speech simultaneously and obtain the clean speech characteristic with fewer parameters.Varieties of noisy speech datasets are tested,including public and self-built sets using noise collected in different environments and radio systems.The results of the simulation experiment on the VoiceBank-DEMAND dataset indicate that the proposed method outperforms similar speech enhancement models in terms of objective metrics such as PESQ and STOI.Field experimental results show that the enhancement scheme can effectively suppress different environmental and radio noise types.The performance under low signal-to-noise ratios is superior to that of the same kind of enhancement networks.

Key words: Radio communication, Voice enhancement, Deep separable convolution, Attention mechanism