Monaural Speech Enhancement Based on Attention-Gate Dilated Convolution Network

In supervised speech enhancement, contextual information has an important influence on the estimation of target speech. In order to obtain richer global related features of speech, a new convolution network for speech enhancement on the premise of the smallest possible parameters is designed in this paper. The proposed network contains three parts: encode layer, transfer layer and decode layer. The encode and decode part propose a Two-Dimensional Asymmetric Dilated Residual ( 2D-ADR) module, which can significantly reduce training parameters and expand the receptive field, and improve the model's ability to obtain contextual information. The transfer layer proposes a One-Dimensional Gating Dilated Residual (1DGDR) module, which combines dilated convolution, residual learning and gating mechanism to transfer selectively features and obtain more time-related information. Moreover, the eight 1D-GDR modules are stacked by a dense skip-connection way to enhance the information flow between layers and provide more gradient propagation path. Finally, the corresponding encode and decode layer is connected by skip-connection and attention mechanism is introduced to make the decoding process obtain more robust underlying features. In the experimental part, different parameter settings and comparison methods are used to verify the effectiveness and robustness of the network. By training and testing under 28 kinds of noise, compared with other methods, the proposed method has achieved better objective and subjective metrics with 1.25 million parameters, and has better enhancement effect and generalization ability.