14 citations · 98 across the 27 of their papers we have counts for
6 papers · 1 filter
Two Heads Are Better Than One: A Two-Stage Approach for Monaural Noise Reduction in the Complex Domain
Andong Li, Chengshi Zheng, Renhua Peng +1
In low signal-to-noise ratio conditions, it is difficult to effectively recover the magnitude and phase information simultaneously. To address this problem, this paper proposes a t…
Distributed Node-Specific Block-Diagonal LCMV Beamforming in Wireless Acoustic Sensor Networks
Xinwei Guo, Minmin Yuan, Chengshi Zheng +1
This paper derives the analytical solution of a novel distributed node-specific block-diagonal linearly constrained minimum variance beamformer from the centralized linearly constr…
Dynamic Attention Based Generative Adversarial Network with Phase Post-Processing for Speech Enhancement
Andong Li, Chengshi Zheng, Renhua Peng +2
The generative adversarial networks (GANs) have facilitated the development of speech enhancement recently. Nevertheless, the performance advantage is still limited when compared w…
The IOA System for Deep Noise Suppression Challenge using a Framework Combining Dynamic Attention and Recursive Learning
Andong Li, Chengshi Zheng, Renhua Peng +2
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…
A Recursive Network with Dynamic Attention for Monaural Speech Enhancement
Andong Li, Chengshi Zheng, Cunhang Fan +2
A person tends to generate dynamic attention towards speech under complicated environments. Based on this phenomenon, we propose a framework combining dynamic attention and recursi…
A Time-domain Monaural Speech Enhancement with Feedback Learning
Andong Li, Chengshi Zheng, Linjuan Cheng +2
In this paper, we propose a type of neural network with feedback learning in the time domain called FTNet for monaural speech enhancement, where the proposed network consists of th…