Audio feature ranking for sound-based COVID-19 patient detection

• URL: http://arxiv.org/abs/2104.07128v1
• Date: Wed, 14 Apr 2021 21:06:20 GMT
• Title: Audio feature ranking for sound-based COVID-19 patient detection
• Authors: Julia A. Meister and Khuong An Nguyen and Zhiyuan Luo
• Abstract summary: COVID-19 has emerged as a low-cost, non-invasive, and accessible audio classification method. No application has been approved for official use due to the stringent reliability and accuracy requirements of the critical healthcare setting. We performed an investigation and ranking of 15 audio features, including less well-known ones. The results were verified on two independent COVID-19 sound datasets.
• Score: 1.7188280334580195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio classification using breath and cough samples has recently emerged as a low-cost, non-invasive, and accessible COVID-19 screening method. However, no application has been approved for official use at the time of writing due to the stringent reliability and accuracy requirements of the critical healthcare setting. To support the development of the Machine Learning classification models, we performed an extensive comparative investigation and ranking of 15 audio features, including less well-known ones. The results were verified on two independent COVID-19 sound datasets. By using the identified top-performing features, we have increased the COVID-19 classification accuracy by up to 17% on the Cambridge dataset, and up to 10% on the Coswara dataset, compared to the original baseline accuracy without our feature ranking.

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