111 citations · 261 across the 15 of their papers we have counts for
16 papers
Multi-scale temporal-frequency attention for music source separation
Lianwu Chen, Xiguang Zheng, Chen Zhang +2
In recent years, deep neural networks (DNNs) based approaches have achieved the start-of-the-art performance for music source separation (MSS). Although previous methods have addre…
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…
Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation
Yong Xu, Zhuohuang Zhang, Meng Yu +2
Although the conventional mask-based minimum variance distortionless response (MVDR) could reduce the non-linear distortion, the residual noise level of the MVDR separated speech i…
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Zhuohuang Zhang, Yong Xu, Meng Yu +4
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are har…
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…
Multi-modal Multi-channel Target Speech Separation
Rongzhi Gu, Shi-Xiong Zhang, Yong Xu +3
Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers. Previously the use of visual modality for target speech sep…
Overlapped speech recognition from a jointly learned multi-channel neural speech extraction and representation
Bo Wu, Meng Yu, Lianwu Chen +3
We propose an end-to-end joint optimization framework of a multi-channel neural speech extraction and deep acoustic model without mel-filterbank (FBANK) extraction for overlapped s…
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
Zhenyu Tang, Lianwu Chen, Bo Wu +2
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-ba…
End-to-End Multi-Channel Speech Separation
Rongzhi Gu, Jian Wu, Shi-Xiong Zhang +6
The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end…
Improved Speaker-Dependent Separation for CHiME-5 Challenge
Jian Wu, Yong Xu, Shi-Xiong Zhang +4
This paper summarizes several follow-up contributions for improving our submitted NWPU speaker-dependent system for CHiME-5 challenge, which aims to solve the problem of multi-chan…
Time Domain Audio Visual Speech Separation
Jian Wu, Yong Xu, Shi-Xiong Zhang +4
Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement. This paper introduces a new…
Deep Extractor Network for Target Speaker Recovery From Single Channel Speech Mixtures
Jun Wang, Jie Chen, Dan Su +4
Speaker-aware source separation methods are promising workarounds for major difficulties such as arbitrary source permutation and unknown number of sources. However, it remains cha…