Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
This work addresses the need for accessible COVID-19 detection tools for the general public, though it appears incremental as it builds on existing signal processing and deep learning techniques.
The paper tackled the problem of quickly triaging COVID-19 infections by developing a machine learning method that uses recordings of breath, voice, and cough from consumer devices, achieving robust performance on both open-source datasets and noisy real-world data during beta testing.
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.