Neeraj Sharma(Carnegie Mellon University), Prashant Krishnan(Indian Institute of Science), Rohit Kumar(Indian Institute of Science), Shreyas Ramoji(Indian Institute of Science), Srikanth Raj Chetupalli(Indian Institute of Science, Bangalore), Nirmala R(Indian Institute of Science), Prasanta Ghosh(Assistant Professor, EE, IISc) and Sriram Ganapathy(Indian Institute of Science, Bangalore, India, 560012)
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.