80 citations · 84 across the 6 of their papers we have counts for
10 papers · 1 filter
A two-step backward compatible fullband speech enhancement system
Xu Zhang, Lianwu Chen, Xiguang Zheng +4
Speech enhancement methods based on deep learning have surpassed traditional methods. While many of these new approaches are operating on the wideband (16kHz) sample rate, a new fu…
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
Helin Wang, Bo Wu, Lianwu Chen +7
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments. We propose…
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Zhuohuang Zhang, Yong Xu, Meng Yu +3
Speech separation algorithms are often used to separate the target speech from other interfering sources. However, purely neural network based speech separation systems often cause…
Distortionless Multi-Channel Target Speech Enhancement for Overlapped Speech Recognition
Bo Wu, Meng Yu, Lianwu Chen +4
Speech enhancement techniques based on deep learning have brought significant improvement on speech quality and intelligibility. Nevertheless, a large gain in speech quality measur…
Neural Spatio-Temporal Beamformer for Target Speech Separation
Yong Xu, Meng Yu, Shi-Xiong Zhang +4
Purely neural network (NN) based speech separation and enhancement methods, although can achieve good objective scores, inevitably cause nonlinear speech distortions that are harmf…
Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning
Rongzhi Gu, Shi-Xiong Zhang, Lianwu Chen +5
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. Howe…