2 citations · 2 across the 6 of their papers we have counts for
6 papers
SNR-Progressive Model with Harmonic Compensation for Low-SNR Speech Enhancement
Zhongshu Hou, Tong Lei, Qinwen Hu +3
Despite significant progress made in the last decade, deep neural network (DNN) based speech enhancement (SE) still faces the challenge of notable degradation in the quality of rec…
Personalized speech enhancement combining band-split RNN and speaker attentive module
Xiaohuai Le, Li Chen, Chao He +4
Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech. Previous works introduce the speaker embedding into spe…
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…