  Cough audio signal classification is a potentially useful tool in screening
for respiratory disorders, such as COVID-19. Since it is dangerous to collect
data from patients with such contagious diseases, many research teams have
turned to crowdsourcing to quickly gather cough sound data, as it was done to
generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians
to diagnose the underlying diseases present in a limited number of uploaded
recordings. However, this approach suffers from potential mislabeling of the
coughs, as well as notable disagreement between experts. In this work, we use a
semi-supervised learning (SSL) approach to improve the labeling consistency of
the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound
classification. First, we leverage existing SSL expert knowledge aggregation
techniques to overcome the labeling inconsistencies and sparsity in the
dataset. Next, our SSL approach is used to identify a subsample of re-labeled
COUGHVID audio samples that can be used to train or augment future cough
classification models. The consistency of the re-labeled data is demonstrated
in that it exhibits a high degree of class separability, 3x higher than that of
the user-labeled data, despite the expert label inconsistency present in the
original dataset. Furthermore, the spectral differences in the user-labeled
audio segments are amplified in the re-labeled data, resulting in significantly
different power spectral densities between healthy and COVID-19 coughs, which
demonstrates both the increased consistency of the new dataset and its
explainability from an acoustic perspective. Finally, we demonstrate how the
re-labeled dataset can be used to train a cough classifier. This SSL approach
can be used to combine the medical knowledge of several experts to improve the
database consistency for any diagnostic classification task.
