A Recursive Network with Dynamic Attention for Monaural Speech Enhancement
Andong Li, Chengshi Zheng, Cunhang Fan, Renhua Peng, Xiaodong Li · 2020
For continuous speech processing, dynamic attention is helpful in preferential processing, which has already been shown by the auditory dynamic attending theory.Accordingly, we propose a framework combining dynamic attention and recursive learning together for monaural speech enhancement.Apart from a major noise reduction network, we design a separated sub-network, which adaptively generates the attention distribution to control the information flow throughout the major network.Recursive learning is introduced to dynamically reduce the number of trainable parameters by reusing a network for multiple stages, where the intermediate output in each stage is corrected with a memory mechanism.By doing so, a more flexible and better estimation can be obtained.We conduct experiments on TIMIT corpus.Experimental results show that the proposed architecture obtains consistently better performance than recent state-of-the-art models in terms of both PESQ and STOI scores.The code is provided at https://github.com/Andong-Li-speech/DARCN.