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

TL;DR

This paper presents a practical machine learning-based system that uses recordings of breath, voice, and cough, combined with signal processing, to detect COVID-19 infection efficiently on consumer devices.

Contribution

It introduces a novel integrated approach combining signal denoising, cough detection, and deep learning for COVID-19 detection from audio recordings, deployed via a mobile app.

Findings

Robust performance on open-source datasets

Effective detection in noisy real-world data

Successful deployment in a mobile application

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.