THE SECOND DICOVA CHALLENGE: DATASET AND PERFORMANCE ANALYSIS FOR DIAGNOSIS OF COVID-19 USING ACOUSTICS
47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
; 2022-May:556-560, 2022.
Article
in English
| Scopus | ID: covidwho-1891398
ABSTRACT
The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the 21 participating teams in the leaderboard is also presented. © 2022 IEEE
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Scopus
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English
Journal:
47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Year:
2022
Document Type:
Article
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