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 $16$ participating teams in the leaderboard is also presented.

翻译：利用声学(DiCOVA)对COVID-19进行第二次诊断(COVID-19)的挑战,旨在加速对COVID-19的声学探测进行研究,这是声学、信号处理、机器学习和保健交汇处的一个专题,本文介绍了挑战的细节,这是研究人员分析由呼吸、咳嗽和语音信号组成的录音数据集的公开呼吁,这些数据来自有COVID-19感染和没有感染COVID-19的个人,而挑战的任务是分两类分类。开发成套录音来自965人(172 COVID-19正数),而评价包含471人的数据(71 COVID-19正数),挑战包括四个轨道,每个声学、言语和呼吸类别都有一个,第四个凝聚轨道,基线系统也发布给与会者基准。在这份文件中,我们概述了挑战、数据收集理由和基线系统。此外,还介绍了领导板参与小组提交的系统业绩分析。