A large-scale and PCR-referenced vocal audio dataset for COVID-19

Budd, Jobie and Baker, Kieran and Karoune, Emma and Coppock, Harry and Patel, Selina and Payne, Richard and Tendero Cañadas, Ana and Titcomb, Alexander and Hurley, David and Egglestone, Sabrina and Butler, Lorraine and Mellor, Jonathon and Nicholson, George and Kiskin, Ivan and Koutra, Vasiliki and Jersakova, Radka and McKendry, Rachel A. and Diggle, Peter and Richardson, Sylvia and Schuller, Björn W. and Gilmour, Steven and Pigoli, Davide and Roberts, Stephen and Packham, Josef and Thornley, Tracey and Holmes, Chris (2024) A large-scale and PCR-referenced vocal audio dataset for COVID-19. Scientific Data, 11 (1): 700. ISSN 2052-4463

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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.

Item Type:
Journal Article
Journal or Publication Title:
Scientific Data
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1000
Subjects:
?? general ??
ID Code:
221929
Deposited By:
Deposited On:
10 Jul 2024 14:45
Refereed?:
Yes
Published?:
Published
Last Modified:
10 Jul 2024 14:45