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Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough (2107.10716v2)

Published 12 Jul 2021 in eess.SP, cs.LG, cs.SD, and eess.AS

Abstract: The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions. In this paper we propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection. The application showed robust performance on both open sourced datasets and on the noisy data collected during beta testing by the end users.

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Authors (7)
  1. Alexander Ponomarchuk (3 papers)
  2. Ilya Burenko (4 papers)
  3. Elian Malkin (2 papers)
  4. Ivan Nazarov (17 papers)
  5. Vladimir Kokh (11 papers)
  6. Manvel Avetisian (7 papers)
  7. Leonid Zhukov (11 papers)
Citations (38)

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