Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
This provides a resource for developing AI models for COVID-19 detection, but it is incremental as it builds on existing respiratory sound datasets.
The authors introduced the Coswara dataset, containing respiratory sounds and metadata from 2635 individuals, to tackle remote screening of SARS-CoV-2 infection, and trained a BLSTM-based COVID-19 classifier to analyze biases across demographic groups.
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.