  The COVID-19 pandemic presents global challenges transcending boundaries of
country, race, religion, and economy. The current gold standard method for
COVID-19 detection is the reverse transcription polymerase chain reaction
(RT-PCR) testing. However, this method is expensive, time-consuming, and
violates social distancing. Also, as the pandemic is expected to stay for a
while, there is a need for an alternate diagnosis tool which overcomes these
limitations, and is deployable at a large scale. The prominent symptoms of
COVID-19 include cough and breathing difficulties. We foresee that respiratory
sounds, when analyzed using machine learning techniques, can provide useful
insights, enabling the design of a diagnostic tool. Towards this, the paper
presents an early effort in creating (and analyzing) a database, called
Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound
samples are collected via worldwide crowdsourcing using a website application.
The curated dataset is released as open access. As the pandemic is evolving,
the data collection and analysis is a work in progress. We believe that
insights from analysis of Coswara can be effective in enabling sound based
technology solutions for point-of-care diagnosis of respiratory infection, and
in the near future this can help to diagnose COVID-19.
