  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.
