COVID 19 Cough

Research on COVID-19 coughs focuses on developing accurate and efficient methods for detecting the disease remotely using audio analysis. Current approaches leverage machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning techniques, often trained on large datasets of cough recordings to distinguish COVID-19 coughs from healthy or other respiratory illness coughs. These efforts aim to create accessible, cost-effective screening tools, potentially improving early detection and disease management, particularly in resource-limited settings. The development of synthetic cough data is also being explored to address the limitations of existing datasets.

Papers

November 12, 2023

Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models
Sudip Vhaduri, Seungyeon Paik, Jessica E Huber
Transfer Learning Covid 19 Patient Cough Detection Patient Cough Healthy Subject COVID 19 Cough

June 2, 2023

Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains
Jinjin Cai, Sudip Vhaduri, Xiao Luo
Contrastive Learning Covid 19 Unlabeled Data Domain Adaptive Exhaled Breath COVID 19 Cough

May 5, 2023

Synthesizing Cough Audio with GAN for COVID-19 Detection
Yahya Saleh
Generative Model GAN Model COVID 19 Detection Medical Datasets Data Synthesis Synthesized Image Cough Audio COVID 19 Cough

May 11, 2022

Automatic Tuberculosis and COVID-19 cough classification using deep learning
Madhurananda Pahar, Marisa Klopper, Byron Reeve, Rob Warren, Grant Theron, Andreas Diacon, Thomas Niesler
Deep Learning Cough Detection Cough Audio Patient Cough COVID 19 Cough

February 17, 2022

A Summary of the ComParE COVID-19 Challenges
Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton Batliner, Cecilia Mascolo, Björn W. Schuller
Covid 19 Covid 19 Pandemic Speech Recording Respiratory Sound COVID 19 Cough

COVID 19 Cough

Papers

Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models

Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains

Synthesizing Cough Audio with GAN for COVID-19 Detection

Automatic Tuberculosis and COVID-19 cough classification using deep learning

A Summary of the ComParE COVID-19 Challenges