  This paper presents the Coswara dataset, a dataset containing diverse set of
respiratory sounds and rich meta-data, recorded between April-2020 and
February-2022 from 2635 individuals (1819 SARS-CoV-2 negative, 674 positive,
and 142 recovered subjects). The respiratory sounds contained nine sound
categories associated with variants of breathing, cough and speech. The rich
metadata contained demographic information associated with age, gender and
geographic location, as well as the health information relating to the
symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test
status. Our study is the first of its kind to manually annotate the audio
quality of the entire dataset (amounting to 65~hours) through manual listening.
The paper summarizes the data collection procedure, demographic, symptoms and
audio data information. A COVID-19 classifier based on bi-directional long
short-term (BLSTM) architecture, is trained and evaluated on the different
population sub-groups contained in the dataset to understand the bias/fairness
of the model. This enabled the analysis of the impact of gender, geographic
location, date of recording, and language proficiency on the COVID-19 detection
performance.
