  Audio signals generated by the human body (e.g., sighs, breathing, heart,
digestion, vibration sounds) have routinely been used by clinicians as
indicators to diagnose disease or assess disease progression. Until recently,
such signals were usually collected through manual auscultation at scheduled
visits. Research has now started to use digital technology to gather bodily
sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory
examination, which could then be used for automatic analysis. Some initial work
shows promise in detecting diagnostic signals of COVID-19 from voice and
coughs. In this paper we describe our data analysis over a large-scale
crowdsourced dataset of respiratory sounds collected to aid diagnosis of
COVID-19. We use coughs and breathing to understand how discernible COVID-19
sounds are from those in asthma or healthy controls. Our results show that even
a simple binary machine learning classifier is able to classify correctly
healthy and COVID-19 sounds. We also show how we distinguish a user who tested
positive for COVID-19 and has a cough from a healthy user with a cough, and
users who tested positive for COVID-19 and have a cough from users with asthma
and a cough. Our models achieve an AUC of above 80% across all tasks. These
results are preliminary and only scratch the surface of the potential of this
type of data and audio-based machine learning. This work opens the door to
further investigation of how automatically analysed respiratory patterns could
be used as pre-screening signals to aid COVID-19 diagnosis.
