Perceive and predict: self-supervised speech representation based loss functions for speech enhancement

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Close, G., Ravenscroft, W., Hain, T. orcid.org/0000-0003-0939-3464 et al. (1 more author) (2023) Perceive and predict: self-supervised speech representation based loss functions for speech enhancement. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Proceedings. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 04-10 Jun 2023, Rhodes Island, Greece. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9781728163284

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Item Type: Proceedings Paper
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© 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: self-supervised representations; speech enhancement; loss functions; neural networks
Dates:
  • Published: 5 May 2023
  • Published (online): 5 May 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
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Grant number
Engineering and Physical Sciences Research Council
EP/S023062/1
Depositing User: Symplectic Sheffield
Date Deposited: 21 Jun 2023 09:24
Last Modified: 04 Sep 2023 12:43
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/icassp49357.2023.10095666
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