학술논문
A large-scale and PCR-referenced vocal audio dataset for COVID-19
Document Type
Working Paper
Author
Budd, Jobie; Baker, Kieran; Karoune, Emma; Coppock, Harry; Patel, Selina; Cañadas, Ana Tendero; Titcomb, Alexander; Payne, Richard; 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
Source
Subject
Language
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 to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and 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 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.
Comment: 39 pages, 4 figures
Comment: 39 pages, 4 figures