14 citations · 66 across the 17 of their papers we have counts for
13 papers
Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement
Andong Li, Wenzhe Liu, Chengshi Zheng +1
The spatial covariance matrix has been considered to be significant for beamformers. Standing upon the intersection of traditional beamformers and deep neural networks, we propose…
Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization
Lu Zhang, Mingjiang Wang, Andong Li +2
Recent single-channel speech enhancement methods based on deep neural networks (DNNs) have achieved remarkable results, but there are still generalization problems in real scenes.…
A Simultaneous Denoising and Dereverberation Framework with Target Decoupling
Andong Li, Wenzhe Liu, Xiaoxue Luo +3
Background noise and room reverberation are regarded as two major factors to degrade the subjective speech quality. In this paper, we propose an integrated framework to address sim…
Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement
Andong Li, Chengshi Zheng, Lu Zhang +1
The capability of the human to pay attention to both coarse and fine-grained regions has been applied to computer vision tasks. Motivated by that, we propose a collaborative learni…
Learning to Inference with Early Exit in the Progressive Speech Enhancement
Andong Li, Chengshi Zheng, Lu Zhang +1
In real scenarios, it is often necessary and significant to control the inference speed of speech enhancement systems under different conditions. To this end, we propose a stage-wi…
ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network
Andong Li, Wenzhe Liu, Xiaoxue Luo +2
It remains a tough challenge to recover the speech signals contaminated by various noises under real acoustic environments. To this end, we propose a novel system for denoising in…