Audio Feature Ranking for Sound-Based COVID-19 Patient Detection

Julia Meister, Khuong Nguyen, Zhiyuan Luo

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Audio classification using breath and cough samples has recently emerged as a low-cost, non-invasive, and accessible COVID-19 screening method. However, a comprehensive survey shows that 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 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 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 accuracies without our feature ranking.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (volume 13566)
Subtitle of host publicationProgress in Artificial Intelligence. EPIA 2022.
EditorsG. Marreiros, B. Martins, A. Paiva, B. Ribeiro, A. Sardinha
PublisherSpringer, [Cham]
Pages146–158
Volume13566
ISBN (Electronic)978-3-031-16474-3
ISBN (Print)978-3-031-16473-6
DOIs
Publication statusPublished - 2022

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