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; Diggle, Peter; Richardson, Sylvia; Schuller, Björn; Gilmour, Steven; Pigoli, Davide; Roberts, Stephen; Packham, Josef; Thornley, Tracey; Holmes, Chris

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.

• Journal Article Type: Article
• Acceptance Date: Jun 10, 2024
• Online Publication Date: Jun 27, 2024
• Publication Date: Jun 27, 2024
• Deposit Date: Apr 10, 2024
• Publicly Available Date: Jul 8, 2024
• Electronic ISSN: 2052-4463
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Article Number: 700
• DOI: https://doi.org/10.1038/s41597-024-03492-w
• Public URL: https://nottingham-repository.worktribe.com/output/33561144
• Publisher URL: https://www.nature.com/articles/s41597-024-03492-w