面向电台通信的CLU-Net语音增强网络

CLU-Net Speech Enhancement Network for Radio Communication

为了消除电台系统中的环境噪声和信道噪声对语音通信质量的不利影响,提升电台语音通信的质量,提出了一种基于联合通道注意力与长短时记忆网络(Long Short Term Memory,LSTM)的深度可分离U形网络CLU-Net(Channel Attention and LSTM-based U-Net)。该网络采用深度可分离卷积实现低复杂度的特征提取,联合利用注意力机制和LSTM同时关注语音通道特征和长时上下文联系,在参数量较少的情况下实现对干净语音特征的关注。在公开与实测数据集上进行多组对比实验,仿真结果表明,所提方法在VoiceBank-DEMAND数据集上的PESQ和STOI等指标得分优于同类语音增强模型。实测实验结果表明,所提CLU-Net增强框架能够有效抑制环境噪声与信道噪声,在低信噪比条件下的增强性能优于其他同类型的增强网络。

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.