Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis

Abstract

The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.

A description of Coswara dataset to evaluate COVID-19 diagnosis using respiratory sounds

References in corpus (1)

• Scikit-learn: Machine Learning in Python

Cited by in corpus (32)

• Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
• COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
• COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features
• A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels
• Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
• Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment
• From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
• Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
• QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds
• Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification
• COVID-19 Patient Detection from Telephone Quality Speech Data
• Automatic Tuberculosis and COVID-19 cough classification using deep learning
• Audio feature ranking for sound-based COVID-19 patient detection
• Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
• Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data
• Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19
• End-2-End COVID-19 Detection from Breath & Cough Audio
• COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
• An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification
• Clinical Landscape of COVID-19 Testing: Difficult Choices
• The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
• COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals
• Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds
• Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data
• Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses
• Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework
• Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
• Impact of data-splits on generalization: Identifying COVID-19 from cough and context
• Using Self-Supervised Feature Extractors with Attention for Automatic COVID-19 Detection from Speech
• SRIB Submission to Interspeech 2021 DiCOVA Challenge
• EIHW-MTG: Second DiCOVA Challenge System Report
• Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound