{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dicova-challenge-dataset-task-and-baseline","title":"DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics","arxiv_id":"2103.09148","date":"2021-03-16","proceeding":null,"authors":["Ananya Muguli","Lancelot Pinto","Nirmala R.","Neeraj Sharma","Prashant Krishnan","Prasanta Kumar Ghosh","Rohit Kumar","Shrirama Bhat","Srikanth Raj Chetupalli","Sriram Ganapathy","Shreyas Ramoji","Viral Nanda"],"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.","url_abs":"https://arxiv.org/abs/2103.09148v3","url_pdf":"https://arxiv.org/pdf/2103.09148v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dicova-challenge-dataset-task-and-baseline","repo_url":"https://github.com/glam-imperial/coughlime","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"covid-19-detection","task_name":"COVID-19 Diagnosis"}],"methods":[],"datasets_introduced":[{"slug":"dicova","name":"DiCOVA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}