14 citations · 34 across the 10 of their papers we have counts for
5 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…
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…