J
Jun Du
Researcher at University of Science and Technology of China
Publications - 312
Citations - 7585
Jun Du is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Speech enhancement & Artificial neural network. The author has an hindex of 32, co-authored 269 publications receiving 5203 citations. Previous affiliations of Jun Du include Tsinghua University & Nanchang University.
Papers
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Journal ArticleDOI
A regression approach to speech enhancement based on deep neural networks
TL;DR: The proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general, and is effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.
Journal ArticleDOI
An Experimental Study on Speech Enhancement Based on Deep Neural Networks
TL;DR: This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture that tends to achieve significant improvements in terms of various objective quality measures.
Proceedings ArticleDOI
Multiple-target deep learning for LSTM-RNN based speech enhancement
TL;DR: The proposed framework can consistently and significantly improve the objective measures for both speech quality and intelligibility and a novel multiple-target joint learning approach is designed to fully utilize this complementarity.
Journal ArticleDOI
Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition
TL;DR: Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format, significantly outperformed the state-of-the-art method.
Proceedings ArticleDOI
The Second DIHARD Diarization Challenge: Dataset, Task, and Baselines.
Neville Ryant,Kenneth Church,Christopher Cieri,Alejandrina Cristia,Jun Du,Sriram Ganapathy,Mark Liberman +6 more
TL;DR: The second edition of the DIHARD challenge as discussed by the authors was designed to improve the robustness of speaker diarization systems to variation in recording equipment, noise conditions, and conversational domain.