  This paper addresses issues on cough-based COVID-19 detection. We propose a
cross-dataset transfer learning approach to improve the performance of COVID-19
detection by incorporating cough detection, cough segmentation, and data
augmentation. The first aimed at removing non-cough signals and cough signals
with low probability. The second aimed at segregating several coughs in a
waveform into individual coughs. The third aimed at increasing the number of
samples for the deep learning model. These three processing blocks are
important as our finding revealed a large margin of improvement relative to the
baseline methods without these blocks. An ablation study is conducted to
optimize hyperparameters and it was found that alpha mixup is an important
factor among others in improving the model performance via this augmentation
method. A summary of this study with previous studies on the same evaluation
set was given to gain insights into different methods of cough-based COVID-19
detection.
