Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Audio feature ranking for sound-based COVID-19 patient detection   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.

22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci