14 citations · 111 across the 35 of their papers we have counts for
11 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…
MDNet: Learning Monaural Speech Enhancement from Deep Prior Gradient
Andong Li, Chengshi Zheng, Ziyang Zhang +1
While traditional statistical signal processing model-based methods can derive the optimal estimators relying on specific statistical assumptions, current learning-based methods fu…
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…
Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer
Chengshi Zheng, Wenzhe Liu, Andong Li +2
It is highly desirable that speech enhancement algorithms can achieve good performance while keeping low latency for many applications, such as digital hearing aids, acoustically t…
A Neural Beam Filter for Real-time Multi-channel Speech Enhancement
Wenzhe Liu, Andong Li, Chengshi Zheng +1
Most deep learning-based multi-channel speech enhancement methods focus on designing a set of beamforming coefficients to directly filter the low signal-to-noise ratio signals rece…
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…
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
Linjuan Cheng, Chengshi Zheng, Andong Li +3
In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, var…