  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.
