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A large-scale and PCR-referenced vocal audio dataset for COVID-19.
Budd, Jobie; Baker, Kieran; Karoune, Emma; Coppock, Harry; Patel, Selina; Payne, Richard; Tendero Cañadas, Ana; Titcomb, Alexander; Hurley, David; Egglestone, Sabrina; Butler, Lorraine; Mellor, Jonathon; Nicholson, George; Kiskin, Ivan; Koutra, Vasiliki; Jersakova, Radka; McKendry, Rachel A; Diggle, Peter; Richardson, Sylvia; Schuller, Björn W; Gilmour, Steven; Pigoli, Davide; Roberts, Stephen; Packham, Josef; Thornley, Tracey; Holmes, Chris.
Afiliación
  • Budd J; London Centre for Nanotechnology, University College London, London, UK.
  • Baker K; Division of Medicine, University College London, London, UK.
  • Karoune E; King's College London, London, UK.
  • Coppock H; The Alan Turing Institute, London, UK.
  • Patel S; The Alan Turing Institute, London, UK. ekaroune@turing.ac.uk.
  • Payne R; The Alan Turing Institute, London, UK.
  • Tendero Cañadas A; Imperial College London, London, UK.
  • Titcomb A; UK Health Security Agency, London, UK.
  • Hurley D; Institute of Health Informatics, University College London, London, UK.
  • Egglestone S; UK Health Security Agency, London, UK.
  • Butler L; UK Health Security Agency, London, UK.
  • Mellor J; Centre for Stress and Age-Related Disease, School of Applied Sciences, University of Brighton, Brighton, UK.
  • Nicholson G; UK Health Security Agency, London, UK.
  • Kiskin I; UK Health Security Agency, London, UK.
  • Koutra V; UK Health Security Agency, London, UK.
  • Jersakova R; UK Health Security Agency, London, UK.
  • McKendry RA; UK Health Security Agency, London, UK.
  • Diggle P; The Alan Turing Institute, London, UK.
  • Richardson S; University of Oxford, Oxford, UK.
  • Schuller BW; University of Surrey, Guildford, UK.
  • Gilmour S; The Surrey Institute for People-Centred AI, Centre for Vision, Speech and Signal Processing, Guildford, UK.
  • Pigoli D; King's College London, London, UK.
  • Roberts S; The Alan Turing Institute, London, UK.
  • Packham J; The Alan Turing Institute, London, UK.
  • Thornley T; London Centre for Nanotechnology, University College London, London, UK.
  • Holmes C; Division of Medicine, University College London, London, UK.
Sci Data ; 11(1): 700, 2024 Jun 27.
Article en En | MEDLINE | ID: mdl-38937483
ABSTRACT
The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up and help beat coronavirus' digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido