2022
DOI: 10.1109/access.2022.3211295
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Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline

Abstract: As long as the COVID-19 pandemic is still active in most countries worldwide, rapid diagnostic continues to be crucial to mitigate the impact of seasonal infection waves. Commercialized rapid antigen self-tests proved they cannot handle the most demanding periods, lacking availability and leading to cost rises. Thus, developing a non-invasive, costless, and more decentralized technology capable of giving people feedback about the COVID-19 infection probability would fill these gaps. This paper explores a sound… Show more

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Cited by 2 publications
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“…Matias et al 9 used crowdsourced databases combined with quality assessment algorithms of voice recordings to overcome the above mentioned issues and detect SARS-CoV-2 infection with a more reliable and less noisy dataset. Such an approach reached accuracy values ranging from 75% to 84% on Coswara, and from 67% to 81% on a sub-set of COVID-19 Sounds dataset.…”
Section: Commentarymentioning
confidence: 99%
“…Matias et al 9 used crowdsourced databases combined with quality assessment algorithms of voice recordings to overcome the above mentioned issues and detect SARS-CoV-2 infection with a more reliable and less noisy dataset. Such an approach reached accuracy values ranging from 75% to 84% on Coswara, and from 67% to 81% on a sub-set of COVID-19 Sounds dataset.…”
Section: Commentarymentioning
confidence: 99%