Sharma, N and Krishnan, P and Kumar, R and Ramoji, S and Chetupalli, SR and Nirmala, R and Kumar Ghosh, P and Ganapathy, S (2020) Coswara - A database of breathing, cough, and voice sounds for COVID-19 diagnosis. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 25 October 2020 through 29 October 2020, Shanghai; China, pp. 4811-4815.
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Abstract
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. © 2020 ISCA
| Item Type: | Conference Paper |
|---|---|
| Publication: | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Publisher: | International Speech Communication Association |
| Additional Information: | cited By 0; Conference of 21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 ; Conference Date: 25 October 2020 Through 29 October 2020; Conference Code:165507 |
| Keywords: | Learning systems; Speech communication, Data collection; Diagnostic tools; Global challenges; Machine learning techniques; Respiratory sounds; Reverse transcription-polymerase chain reaction; Technology solutions; Work in progress, Polymerase chain reaction |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 12 Jan 2021 05:37 |
| Last Modified: | 12 Jan 2021 05:37 |
| URI: | http://eprints.iisc.ac.in/id/eprint/67641 |
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