  Speech enhancement has benefited from the success of deep learning in terms
of intelligibility and perceptual quality. Conventional time-frequency (TF)
domain methods focus on predicting TF-masks or speech spectrum,via a naive
convolution neural network or recurrent neural network.Some recent studies were
based on Complex spectral Mapping convolution recurrent neural network (CRN) .
These models skiped directly from encoder layers' output and decoder layers'
input ,which maybe thoughtless. We proposed an attention mechanism based skip
connection between encoder and decoder layers,namely Complex Spectral Mapping
With Attention Based Convolution Recurrent Neural Network (CARN).Compared with
CRN model,the proposed CARN model improved more than 10% relatively at several
metrics such as PESQ,CBAK,COVL,CSIG and son,and outperformed the place 1st
model in both real time and non-real time track of the DNS Challenge 2020 at
these metrics.
