Deep learning features for robust detection of acoustic events in sleep-disordered breathing

Romero, H., Ma, N., Brown, G. orcid.org/0000-0001-8565-5476 et al. (2 more authors) (2019) Deep learning features for robust detection of acoustic events in sleep-disordered breathing. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May 2019, Brighton, UK. IEEE . ISBN 9781479981311

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Sleep-disordered breathing; deep learning; hidden Markov model; bottleneck features; corpus
Dates:
  • Accepted: 4 February 2019
  • Published (online): 17 April 2019
  • Published: April 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
FunderGrant number
INNOVATE UK (TSB)KTP009905
PASSION FOR LIFE HEALTHCARE (UK) LIMITEDC002
Depositing User: Symplectic Sheffield
Date Deposited: 12 Feb 2019 13:37
Last Modified: 15 Aug 2019 13:03
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: https://doi.org/10.1109/ICASSP.2019.8683099

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