| A regression approach to speech enhancement based on deep neural networks Y Xu, J Du, LR Dai, CH Lee IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (1), 7-19, 2015 | 1454 | 2015 |
| An experimental study on speech enhancement based on deep neural networks Y Xu, J Du, LR Dai, CH Lee IEEE Signal processing letters 21 (1), 65-68, 2014 | 1031 | 2014 |
| Audio source separation and speech enhancement E Vincent, T Virtanen, S Gannot John Wiley & Sons, 2018 | 286 | 2018 |
| On mean absolute error for deep neural network based vector-to-vector regression J Qi, J Du, SM Siniscalchi, X Ma, CH Lee IEEE Signal Processing Letters 27, 1485-1489, 2020 | 243 | 2020 |
| Multiple-target deep learning for LSTM-RNN based speech enhancement L Sun, J Du, LR Dai, CH Lee 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA), 136-140, 2017 | 240 | 2017 |
| Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition J Zhang, J Du, S Zhang, D Liu, Y Hu, J Hu, S Wei, L Dai Pattern Recognition 71, 196-206, 2017 | 222 | 2017 |
| The second dihard diarization challenge: Dataset, task, and baselines N Ryant, K Church, C Cieri, A Cristia, J Du, S Ganapathy, M Liberman INTERSPEECH, 978-982, 2019 | 188 | 2019 |
| Attention based fully convolutional network for speech emotion recognition Y Zhang, J Du, Z Wang, J Zhang, Y Tu 2018 Asia-Pacific Signal and Information Processing Association Annual …, 2018 | 159 | 2018 |
| Multi-scale attention with dense encoder for handwritten mathematical expression recognition J Zhang, J Du, L Dai 2018 24th international conference on pattern recognition (ICPR), 2245-2250, 2018 | 146 | 2018 |
| The third DIHARD diarization challenge N Ryant, P Singh, V Krishnamohan, R Varma, K Church, C Cieri, J Du, ... INTERSPEECH, 3570-3574, 2021 | 134 | 2021 |
| Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement Y Xu, J Du, Z Huang, LR Dai, CH Lee INTERSPEECH, 1508-1512, 2015 | 132 | 2015 |
| Robust speech recognition with speech enhanced deep neural networks J Du, Q Wang, T Gao, Y Xu, LR Dai, CH Lee Fifteenth annual conference of the international speech communication …, 2014 | 123 | 2014 |
| Track, attend, and parse (tap): An end-to-end framework for online handwritten mathematical expression recognition J Zhang, J Du, L Dai IEEE Transactions on Multimedia 21 (1), 221-233, 2018 | 121 | 2018 |
| Textmountain: Accurate scene text detection via instance segmentation Y Zhu, J Du Pattern Recognition 110, 107336, 2021 | 118 | 2021 |
| The USTC-iFlytek system for CHiME-4 challenge J Du, YH Tu, L Sun, F Ma, HK Wang, J Pan, C Liu, JD Chen, CH Lee Proc. CHiME 4 (1), 36-38, 2016 | 115 | 2016 |
| A speech enhancement approach using piecewise linear approximation of an explicit model of environmental distortions. J Du, Q Huo Interspeech, 569-572, 2008 | 107 | 2008 |
| Dynamic noise aware training for speech enhancement based on deep neural networks. Y Xu, J Du, LR Dai, CH Lee Interspeech 1, 2670-2674, 2014 | 102 | 2014 |
| First DIHARD challenge evaluation plan N Ryant, K Church, C Cieri, A Cristia, J Du, S Ganapathy, M Liberman tech. Rep., 2018 | 101 | 2018 |
| A regression approach to single-channel speech separation via high-resolution deep neural networks J Du, Y Tu, LR Dai, CH Lee IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (8), 1424 …, 2016 | 101 | 2016 |
| Joint training of front-end and back-end deep neural networks for robust speech recognition T Gao, J Du, LR Dai, CH Lee 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 92 | 2015 |
