16 citations · 45 across the 15 of their papers we have counts for
8 papers · 1 filter
TaylorBeamixer: Learning Taylor-Inspired All-Neural Multi-Channel Speech Enhancement from Beam-Space Dictionary Perspective
Andong Li, Guochen Yu, Wenzhe Liu +2
Despite the promising performance of existing frame-wise all-neural beamformers in the speech enhancement field, it remains unclear what the underlying mechanism exists. In this pa…
A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem
Andong Li, Guochen Yu, Chengshi Zheng +2
While deep neural networks have facilitated significant advancements in the field of speech enhancement, most existing methods are developed following either empirical or relativel…
TMGAN-PLC: Audio Packet Loss Concealment using Temporal Memory Generative Adversarial Network
Yuansheng Guan, Guochen Yu, Andong Li +2
Real-time communications in packet-switched networks have become widely used in daily communication, while they inevitably suffer from network delays and data losses in constrained…
Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement
Andong Li, Shan You, Guochen Yu +2
While the deep learning techniques promote the rapid development of the speech enhancement (SE) community, most schemes only pursue the performance in a black-box manner and lack a…
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory
Andong Li, Guochen Yu, Chengshi Zheng +1
While existing end-to-end beamformers achieve impressive performance in various front-end speech processing tasks, they usually encapsulate the whole process into a black box and t…
Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement
Guochen Yu, Andong Li, Wenzhe Liu +3
Due to the high computational complexity to model more frequency bands, it is still intractable to conduct real-time full-band speech enhancement based on deep neural networks. Rec…
DMF-Net: A decoupling-style multi-band fusion model for full-band speech enhancement
Guochen Yu, Yuansheng Guan, Weixin Meng +2
For the difficulty and large computational complexity of modeling more frequency bands, full-band speech enhancement based on deep neural networks is still challenging. Previous st…
DBT-Net: Dual-branch federative magnitude and phase estimation with attention-in-attention transformer for monaural speech enhancement
Guochen Yu, Andong Li, Hui Wang +3
The decoupling-style concept begins to ignite in the speech enhancement area, which decouples the original complex spectrum estimation task into multiple easier sub-tasks i.e., mag…