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This technical report describes our system that is submitted to the DeepNoise Suppression Challenge and presents the results for the non-real-timetrack. To refine the estimation results stage by stage, we utilize recursivelearning, a type of training protocol which aggravates the information throughmultiple stages with a memory mechanism. The attention generator network isdesigned to dynamically control the feature distribution of the noise reductionnetwork. To improve the phase recovery accuracy, we take the complex spectralmapping procedure by decoding both real and imaginary spectra. For the finalblind test set, the average MOS improvements of the submitted system innoreverb, reverb, and realrec categories are 0.49, 0.24, and 0.36,respectively.

The IOA System for Deep Noise Suppression Challenge using a Framework Combining Dynamic Attention and Recursive Learning