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

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arxiv 2212.07738 v4 pith:XDE52VHL submitted 2022-12-15 cs.SD cs.LGeess.AS

classification cs.SDcs.LGeess.AS
keywords audiodatasetparticipantssars-cov-2vocalcovid-19linkedrespiratory
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning

    cs.SD 2025-08 conditional novelty 4.0 of 10

    A self-supervised masked-spectrogram pretraining method for cough audio produces representations that match or exceed AudioSet-pretrained AST on three cough classification tasks.

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