# Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection arXiv: https://arxiv.org/abs/2305.12741 ยท HTML: https://mlsanity.com/paper/2305.12741/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 38 references that are in the map. - First posted on arXiv 22 May 2023. ## Builds on (most cited first, 12 of 12) - [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020) - [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020) - [DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics](https://mlsanity.com/paper/2103.09148/) (2021) - [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021) - [Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing](https://mlsanity.com/paper/2010.02417/) (2020) - [A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels](https://mlsanity.com/paper/2111.05895/) (2021) - [COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features](https://mlsanity.com/paper/2104.02477/) (2021) - [IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples](https://mlsanity.com/paper/2104.13247/) (2021) - [The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics](https://mlsanity.com/paper/2110.01177/) (2021) - [Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough](https://mlsanity.com/paper/2107.10716/) (2021) - [Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge](https://mlsanity.com/paper/2106.10997/) (2021) - [Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals](https://mlsanity.com/paper/2206.12309/) (2022) ## Built on by (most cited first, 22 of 22) - [Multi-modal Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2503.13709/) (2025) - [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024) - [HeAR -- Health Acoustic Representations](https://mlsanity.com/paper/2403.02522/) (2024) - [RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction](https://mlsanity.com/paper/2410.05361/) (2024) - [Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation](https://mlsanity.com/paper/2506.17442/) (2025) - [Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals](https://mlsanity.com/paper/2309.05843/) (2023) - [Voice EHR: Introducing Multimodal Audio Data for Health](https://mlsanity.com/paper/2404.01620/) (2024) - [Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach](https://mlsanity.com/paper/2409.11123/) (2024) - [XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization](https://mlsanity.com/paper/2508.14949/) (2025) - [Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation](https://mlsanity.com/paper/2602.02955/) (2026) - [Wireless Earphone-based Real-Time Monitoring of Breathing Exercises: A Deep Learning Approach](https://mlsanity.com/paper/2404.10310/) (2024) - [A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease](https://mlsanity.com/paper/2508.16237/) (2025) - [Fine-tuning Pre-trained Audio Models for COVID-19 Detection: A Technical Report](https://mlsanity.com/paper/2511.14939/) (2025) - [RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity](https://mlsanity.com/paper/2602.18452/) (2026) - [RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering](https://mlsanity.com/paper/2603.06542/) (2026) - [Adaptive Discovery of Interpretable Audio Attributes with Multimodal LLMs for Low-Resource Classification](https://mlsanity.com/paper/2603.06991/) (2026) - [Cough activity detection for automatic tuberculosis screening](https://mlsanity.com/paper/2603.11241/) (2026) - [MultiSense-Pneumo: A Multimodal Learning Framework for Pneumonia Screening in Resource-Constrained Settings](https://mlsanity.com/paper/2605.02207/) (2026) - [Beyond Classification: A Cough Regression Benchmark for Respiratory Acoustic Foundation Models](https://mlsanity.com/paper/2606.15436/) (2026) - [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026) - [Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment](https://mlsanity.com/paper/2609.00055/) (2026) - [BreathGRU: A Novel Semi-Supervised Bidirectional Gated Recurrent Unit Framework for Speech and Breath Segmentation for Respiratory Audio](https://mlsanity.com/paper/2609.31165/) (2026) ## Abstract (arXiv) 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.