DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics
This work introduces a benchmark for researchers in speech and audio processing to develop methods for COVID-19 diagnosis, but it is incremental as it builds on existing acoustic health research.
The DiCOVA challenge addresses diagnosing COVID-19 using acoustic recordings, providing a dataset and baseline system for two-class classification tasks based on cough sounds and other respiratory recordings.
The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning. This challenge is an open call for researchers to analyze a dataset of sound recordings collected from COVID-19 infected and non-COVID-19 individuals for a two-class classification. These recordings were collected via crowdsourcing from multiple countries, through a website application. The challenge features two tracks, one focusing on cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings. In this paper, we introduce the challenge and provide a detailed description of the task, and present a baseline system for the task.