A summary of the ComParE COVID-19 challenges

  • The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals’ respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

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Metadaten
Author:Harry Coppock, Alican Akman, Christian Bergler, Maurice GerczukORCiD, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin AmiriparianORCiDGND, Alice BairdGND, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton BatlinerGND, Cecilia Mascolo, Björn W. SchullerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1031074
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/103107
ISSN:2673-253XOPAC
Parent Title (English):Frontiers in Digital Health
Publisher:Frontiers Media S.A.
Place of publication:Lausanne
Type:Article
Language:English
Date of first Publication:2023/03/08
Publishing Institution:Universität Augsburg
Release Date:2023/03/22
Tag:COVID-19; machine learning; Digital Health; computer audition; deep learning
Volume:5
First Page:1058163
DOI:https://doi.org/10.3389/fdgth.2023.1058163
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)