COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features

Pahar, M. orcid.org/0000-0002-5926-0144, Klopper, M. orcid.org/0000-0002-9318-8289, Warren, R. orcid.org/0000-0001-5741-7358 et al. (1 more author) (2022) COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features. Computers in Biology and Medicine, 141. 105153. ISSN 0010-4825

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Item Type: Article
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© 2021 Elsevier. This is an author produced version of a paper subsequently published in Computers in Biology and Medicine. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Bottleneck features; Breath; COVID-19; Cough; Speech; Transfer learning; COVID-19; Cough; Humans; Machine Learning; SARS-CoV-2; Speech
Dates:
  • Published: February 2022
  • Published (online): 17 December 2021
  • Accepted: 14 December 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 21 Jan 2025 09:44
Last Modified: 24 Jan 2025 15:13
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: 10.1016/j.compbiomed.2021.105153
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Sustainable Development Goals:
  • Sustainable Development Goals: Goal 3: Good Health and Well-Being
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