Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
This addresses the need for an alternative, scalable diagnostic tool for COVID-19, which is incremental as it focuses on data collection rather than a novel method.
The paper tackles the problem of COVID-19 detection by creating Coswara, a database of breathing, cough, and voice sounds collected via crowdsourcing, with the result being an open-access dataset aimed at enabling machine learning-based diagnostic tools, though no concrete performance numbers are provided as it is an early effort.
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.