TITLE: DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics 

ABSTRACT: 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.
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.
DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics
http://arxiv.org/abs/2103.09148v3