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Automatic Tuberculosis and COVID-19 cough classification using deep learning
This cough data include 1. 68 hours of TB coughs, 18. 54 minutes of COVID-19 coughs and 1. 69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50.
Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring
We present `wake-cough', an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system.
COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features
We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech.
Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment
Objective: The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.
COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone.
