24 citations · 112 across the 18 of their papers we have counts for
18 papers
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…
Inference skipping for more efficient real-time speech enhancement with parallel RNNs
Xiaohuai Le, Tong Lei, Kai Chen +1
Deep neural network (DNN) based speech enhancement models have attracted extensive attention due to their promising performance. However, it is difficult to deploy a powerful DNN i…
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation
Guoliang Cheng, Lele Liao, Kai Chen +3
The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic ech…
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…
DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement
Xiaohuai Le, Hongsheng Chen, Kai Chen +1
The dual-path RNN (DPRNN) was proposed to more effectively model extremely long sequences for speech separation in the time domain. By splitting long sequences to smaller chunks an…