no code implementations • 11 May 2022 • Madhurananda Pahar, Marisa Klopper, Byron Reeve, Rob Warren, Grant Theron, Andreas Diacon, Thomas Niesler
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.
no code implementations • 8 Feb 2022 • Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesler
When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days.
no code implementations • 7 Oct 2021 • Madhurananda Pahar, Marisa Klopper, Byron Reeve, Rob Warren, Grant Theron, Andreas Diacon, Thomas Niesler
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.
no code implementations • 31 Aug 2021 • Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesler
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals.
no code implementations • 2 Apr 2021 • Madhurananda Pahar, Marisa Klopper, Robin Warren, Thomas Niesler
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.
no code implementations • 23 Mar 2021 • Madhurananda Pahar, Marisa Klopper, Byron Reeve, Grant Theron, Rob Warren, Thomas Niesler
Objective: The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.
no code implementations • 9 Feb 2021 • Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesler
Since the need to gather audio is avoided and therefore privacy is inherently protected, and since the accelerometer is attached to the bed and not worn, this form of monitoring may represent a more convenient and readily accepted method of long-term patient cough monitoring.
no code implementations • 2 Dec 2020 • Madhurananda Pahar, Marisa Klopper, Robin Warren, Thomas Niesler
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.