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

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


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 publicationProgress in Artificial Intelligence
Subtitle of host publication21st EPIA Conference on Artificial Intelligence
EditorsGoreti Marreiros, Bruno Martins, Ana Paiva, Bernardete Ribeiro, Alberto Sardinha
Number of pages13
ISBN (Electronic)9783031164743
ISBN (Print)9783031164736
Publication statusPublished - 13 Sep 2022
Event21st EPIA Conference on Artificial Intelligence: Progress in Artificial Intelligence - Lisbon, Portugal
Duration: 31 Aug 20222 Sep 2022

Publication series

NameLecture Notes in Computer Science (LNCS)


Conference21st EPIA Conference on Artificial Intelligence
Abbreviated titleEPIA 2022
Internet address


  • COVID-19 classification
  • Audio event engineering
  • Sound feature ranking


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