  This technical report describes our system that is submitted to the Deep
Noise Suppression Challenge and presents the results for the non-real-time
track. To refine the estimation results stage by stage, we utilize recursive
learning, a type of training protocol which aggravates the information through
multiple stages with a memory mechanism. The attention generator network is
designed to dynamically control the feature distribution of the noise reduction
network. To improve the phase recovery accuracy, we take the complex spectral
mapping procedure by decoding both real and imaginary spectra. For the final
blind test set, the average MOS improvements of the submitted system in
noreverb, reverb, and realrec categories are 0.49, 0.24, and 0.36,
respectively.
