@inproceedings{4e7d2b404aca4981bfc038f0aa588e92,
title = "Time-Frequency Networks for Audio Super-Resolution",
abstract = "Audio super-resolution (a.k.a. bandwidth extension) is the challenging task of increasing the temporal resolution of audio signals. Recent deep networks approaches achieved promising results by modeling the task as a regression problem in either time or frequency domain. In this paper, we introduced Time-Frequency Network (TFNet), a deep network that utilizes supervision in both the time and frequency domain. We proposed a novel model architecture which allows the two domains to be jointly optimized. Results demonstrate that our method outperforms the state-of-the-art both quantitatively and qualitatively.",
keywords = "Audio super-resolution, Bandwidth extension, Deep learning",
author = "Lim, {Teck Yian} and Yeh, {Raymond A.} and Yijia Xu and Do, {Minh N.} and Mark Hasegawa-Johnson",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8462049",
language = "English (US)",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "646--650",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
address = "United States",
}