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Deep Learning Based Speech Separation via NMF-style Reconstructions 期刊论文
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, 2018, 卷号: 26, 期号: 11, 页码: 2043-2055
作者:  Shuai Nie;  Shan Liang;  Wenju Liu;  Xueliang Zhang;  Jianhua Tao
浏览  |  Adobe PDF(2922Kb)  |  收藏  |  浏览/下载：194/76  |  提交时间：2020/10/22
Speech separation  deep neural network (DNN)  nonnegative matrix factorization (NMF)  spectro-temporal structures  
Deep Noise Tracking Network: A Hybrid Signal Processing/Deep Learning Approach to Speech Enhancement 会议论文
, Hyderabad, India, 2018-9-2~2018-9-6
作者:  Nie S(聂帅);  Shan Liang;  Bin Liu;  Yaping Zhang;  Wenju Liu;  Jianhua Tao
浏览  |  Adobe PDF(925Kb)  |  收藏  |  浏览/下载：77/26  |  提交时间：2020/10/22
Sparse Representation Based Image Super-resolution Using Large Patches 期刊论文
CHINESE JOURNAL OF ELECTRONICS, 2018, 卷号: 27, 期号: 4, 页码: 813-820
作者:  Liu Ning;  Zhou Pan;  Liu Wenju;  Ke Dengfeng
收藏  |  浏览/下载：177/0  |  提交时间：2019/12/16
Super resolution  Sparse representations  Binary encoding  
Stochastic Multiple Choice Learning for Acoustic Modeling 会议论文
, Rio de Janeiro, 巴西, 2018-07-08
作者:  Liu, Bin;  Nie, Shuai;  Liang, Shan;  Yang, Zhanlei;  Liu, Wenju
浏览  |  Adobe PDF(529Kb)  |  收藏  |  浏览/下载：174/66  |  提交时间：2020/06/08
Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data 期刊论文
PATTERN RECOGNITION LETTERS, 2018, 卷号: 106, 期号: 无, 页码: 20-26
作者:  Zhang, Yaping;  Liang, Shan;  Nie, Shuai;  Liu, Wenju;  Peng, Shouye
浏览  |  Adobe PDF(756Kb)  |  收藏  |  浏览/下载：283/72  |  提交时间：2018/10/10
Handwritten Character Recognition  Writer-independent Features  Adversarial Feature Learning  Convolutional Neural Network  
Boosting noise robustness of acoustic model via deep adversarial training 会议论文
, 加拿大卡尔加里, 2018-4-15
作者:  Liu, Bin;  Nie, Shuai;  Zhang, Yaping;  Ke, Dengfeng;  Liang, Shan;  Liu, Wenju
浏览  |  Adobe PDF(300Kb)  |  收藏  |  浏览/下载：191/83  |  提交时间：2020/05/15
Robust Speech Recognition  Deep Adversarial Training  Acoustic Model  Generative Adversarial Net