Application of improved U-Net network with attention mechanism in end-to-end speech enhancement

An improved U-Net(ADC-U-Net)network model for end-to-end speech enhancement is designed based on the U-Net network.Compared with the baseline U-Net network,on the one hand,the information loss caused by sampling is reduced by adding the void convolution.On the other hand,the attention mechanism structure is introduced,which combines more contextual information of noisy speech to extract deeper and richer feature information.Compared with traditional speech enhancement methods,the proposed model does not need three steps of feature extraction,feature denoising and speech reconstruction,and avoids the dependence on explicit features.Instead,the network model obtains implicit features through multi-level and multi-scale learning.The quality and intelligibility of enhanced speech are evaluated by several subjective and objective indexes.Experimental data show that the proposed algorithm performs well in noise suppression and adaptability.Compared with the baseline U-Net network and other models,the proposed algorithm demonstrates good speech quality and intelligibility.