A large-scale and PCR-referenced vocal audio dataset for COVID-19
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.
Cite
@article{arxiv.2212.07738,
title = {A large-scale and PCR-referenced vocal audio dataset for COVID-19},
author = {Jobie Budd and Kieran Baker and Emma Karoune and Harry Coppock and Selina Patel and Ana Tendero Cañadas and Alexander Titcomb and Richard Payne and David Hurley and Sabrina Egglestone and Lorraine Butler and Jonathon Mellor and George Nicholson and Ivan Kiskin and Vasiliki Koutra and Radka Jersakova and Rachel A. McKendry and Peter Diggle and Sylvia Richardson and Björn W. Schuller and Steven Gilmour and Davide Pigoli and Stephen Roberts and Josef Packham and Tracey Thornley and Chris Holmes},
journal= {arXiv preprint arXiv:2212.07738},
year = {2023}
}
Comments
39 pages, 4 figures