Audio Feature Ranking for Sound-Based COVID-19 Patient Detection
21st EPIA Conference on Artificial Intelligence, EPIA 2022
; 13566 LNAI:146-158, 2022.
Article
in English
| Scopus | ID: covidwho-2048160
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
21st EPIA Conference on Artificial Intelligence, EPIA 2022
Year:
2022
Document Type:
Article
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