2 citations · 2 across the 5 of their papers we have counts for
5 papers
TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network
Xiaobin Rong, Dahan Wang, Qinwen Hu +3
Universal speech enhancement aims to handle input speech with different distortions and input formats. To tackle this challenge, we present TS-URGENet, a Three-Stage Universal, Rob…
Local spectral attention for full-band speech enhancement
Zhongshu Hou, Qinwen Hu, Kai Chen +1
Attention mechanism has been widely utilized in speech enhancement (SE) because theoretically it can effectively model the inherent connection of signal both in time domain and spe…
Attention does not guarantee best performance in speech enhancement
Zhongshu Hou, Qinwen Hu, Kai Chen +1
Attention mechanism has been widely utilized in speech enhancement (SE) because theoretically it can effectively model the long-term inherent connection of signal both in time doma…
A light-weight full-band speech enhancement model
Qinwen Hu, Zhongshu Hou, Xiaohuai Le +1
Deep neural network based full-band speech enhancement systems face challenges of high demand of computational resources and imbalanced frequency distribution. In this paper, a lig…
A two-stage full-band speech enhancement model with effective spectral compression mapping
Zhongshu Hou, Qinwen Hu, Kai Chen +1
The direct expansion of deep neural network (DNN) based wide-band speech enhancement (SE) to full-band processing faces the challenge of low frequency resolution in low frequency r…