Mark Hasegawa-Johnson
Mark Hasegawa-Johnson
Professor of Electrical and Computer Engineering, University of Illinois
Verified email at - Homepage
Cited by
Cited by
Developmental cognitive neuroscience: An introduction
MH Johnson, MDH de Haan
John Wiley & Sons, 2015
Semantic image inpainting with deep generative models
RA Yeh, C Chen, T Yian Lim, AG Schwing, M Hasegawa-Johnson, MN Do
Proceedings of the IEEE conference on computer vision and pattern …, 2017
Deep learning for monaural speech separation
PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis
2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014
Joint optimization of masks and deep recurrent neural networks for monaural source separation
PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis
IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (12 …, 2015
Autovc: Zero-shot voice style transfer with only autoencoder loss
K Qian, Y Zhang, S Chang, X Yang, M Hasegawa-Johnson
International Conference on Machine Learning, 5210-5219, 2019
Brain anatomy differences in childhood stuttering
SE Chang, KI Erickson, NG Ambrose, MA Hasegawa-Johnson, ...
Neuroimage 39 (3), 1333-1344, 2008
Singing-voice separation from monaural recordings using robust principal component analysis
PS Huang, SD Chen, P Smaragdis, M Hasegawa-Johnson
2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012
Semantic image inpainting with perceptual and contextual losses
R Yeh, C Chen, TY Lim, M Hasegawa-Johnson, MN Do
arXiv preprint arXiv:1607.07539 2 (3), 2016
Dysarthric speech database for universal access research.
H Kim, M Hasegawa-Johnson, A Perlman, JR Gunderson, TS Huang, ...
Interspeech 2008, 1741-1744, 2008
Dilated recurrent neural networks
S Chang, Y Zhang, W Han, M Yu, X Guo, W Tan, X Cui, M Witbrock, ...
Advances in neural information processing systems 30, 2017
Streaming recommender systems
S Chang, Y Zhang, J Tang, D Yin, Y Chang, MA Hasegawa-Johnson, ...
Proceedings of the 26th international conference on world wide web, 381-389, 2017
Signal-based and expectation-based factors in the perception of prosodic prominence
J Cole, Y Mo, M Hasegawa-Johnson
Laboratory Phonology 1 (2), 425-452, 2010
AVICAR: audio-visual speech corpus in a car environment.
B Lee, M Hasegawa-Johnson, C Goudeseune, S Kamdar, S Borys, M Liu, ...
Interspeech, 2489-2492, 2004
Real-world acoustic event detection
X Zhuang, X Zhou, MA Hasegawa-Johnson, TS Huang
Pattern recognition letters 31 (12), 1543-1551, 2010
Regression from patch-kernel
S Yan, X Zhou, M Liu, M Hasegawa-Johnson, TS Huang
2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008
Acoustic fall detection using Gaussian mixture models and GMM supervectors
X Zhuang, J Huang, G Potamianos, M Hasegawa-Johnson
2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009
Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks.
PS Huang, M Kim, M Hasegawa-Johnson, P Smaragdis
ISMIR, 477-482, 2014
Unsupervised speech decomposition via triple information bottleneck
K Qian, Y Zhang, S Chang, M Hasegawa-Johnson, D Cox
International Conference on Machine Learning, 7836-7846, 2020
Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU summer workshop
K Livescu, O Cetin, M Hasegawa-Johnson, S King, C Bartels, N Borges, ...
2007 IEEE International Conference on Acoustics, Speech and Signal …, 2007
Prosodic effects on acoustic cues to stop voicing and place of articulation: Evidence from Radio News speech
J Cole, H Kim, H Choi, M Hasegawa-Johnson
Journal of Phonetics 35 (2), 180-209, 2007
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