{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-large-scale-and-pcr-referenced-vocal-audio","title":"A large-scale and PCR-referenced vocal audio dataset for COVID-19","arxiv_id":"2212.07738","date":"2022-12-15","proceeding":null,"authors":["Jobie Budd","Kieran Baker","Emma Karoune","Harry Coppock","Selina Patel","Ana Tendero Cañadas","Alexander Titcomb","Richard Payne","David Hurley","Sabrina Egglestone","Lorraine Butler","Jonathon Mellor","George Nicholson","Ivan Kiskin","Vasiliki Koutra","Radka Jersakova","Rachel A. McKendry","Peter Diggle","Sylvia Richardson","Björn W. Schuller","Steven Gilmour","Davide Pigoli","Stephen Roberts","Josef Packham","Tracey Thornley","Chris Holmes"],"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.","url_abs":"https://arxiv.org/abs/2212.07738v4","url_pdf":"https://arxiv.org/pdf/2212.07738v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-large-scale-and-pcr-referenced-vocal-audio","repo_url":"https://github.com/alan-turing-institute/turing-rss-health-data-lab-biomedical-acoustic-markers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"survey","task_name":"Survey"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}