About Me
My name is Heming Wang, and I am currently a Ph.D. student in Computer Science and Engineering at the Ohio State University, advised by Prof. DeLiang Wang. Before joining OSU, I received my master degree in Applied Mathematics from University of Waterloo. My research interests focus on speech enhancement, audio super-resolution and machine learning.
- Speech enhancement
- Audio super-resolution
- Machine learning
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PhD in Computer Science and Engineering, in progress
The Ohio State University
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MMath in Applied Mathematics, 2018
University of Waterloo
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BSc in Physics, 2016
University of Waterloo
Recent Publications
H. Wang, Y. Qian, X. Wang, Y. Wang, C. Wang, S. Liu, T. Yoshioka, J. Li and D. L. Wang, “Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in press, 2022.
Y. Wang, J. Li, H. Wang, Y. Qian, C. Wang and Y. Wu, “Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in press, 2022.
H. Wang, X. Zhang and D. L. Wang, “Attention-based Fusion for Bone-conducted and Air-conducted Speech Enhancement in the Complex Domain,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in press, 2022.
H. Wang and D. L. Wang, “Cross-domain Speech Enhancement With A Neural Cascade Architecture,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), in press, 2022.
H. Wang and D. L. Wang, “Neural Cascade Architecture with Triple-domain Loss for Speech Enhancement,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing (IEEE/ACM TASLP), vol. 30, pp. 734-743, 2022.
H. Wang and D. L. Wang, “Towards Robust Speech Super-resolution,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing (IEEE/ACM TASLP), vol. 29, pp. 2058-2066, 2021.
H. Wang and D. L. Wang. “Time-Frequency Loss for CNN Based Speech Super-Resolution,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 861-865, 2020.
H. Wang, R. Mann, and E. R. Vrscay, “A Diffusion-Based Two-Dimensional Empirical Mode Decomposition Algorithm for Image Analysis,” in International Conference Image Analysis and Recognition (ICIAR), pp. 293-305, 2018.