论文标题
使用动态注意力和递归学习的框架，用于深噪声抑制挑战的IOA系统
The IOA System for Deep Noise Suppression Challenge using a Framework Combining Dynamic Attention and Recursive Learning
论文作者
论文摘要
该技术报告描述了我们的系统，该系统已提交给深层噪声挑战，并给出了非实时轨道的结果。为了通过阶段来完善估计结果，我们利用递归学习，这是一种训练协议，它通过记忆机制通过多个阶段加剧信息。注意生成器网络旨在动态控制降噪网络的特征分布。为了提高相恢复精度，我们通过解码真实和虚谱来采用复杂的光谱映射程序。对于最终的盲验测试集，Noreverb，Reverb和Realrec类别中提交系统的平均MOS改进分别为0.49、0.24和0.36。
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.
