Ensemble Learning for Cough-Based Subject-Independent COVID-19 Detection

This paper belongs to the medical acoustics field and presents a solution for COVID-19 detection based on the cough sound events. Unfortunately, the use of RT-PCR Molecular Swab tests for the diagnosis of COVID-19 is associated with considerable cost, is based on availability of suitable equipment, requires a specific time period to produce the result, let alone the potential errors in the execution of the tests. Interestingly, in addition to Swab tests, cough sound events could facilitate the detection of COVID-19. Currently, there is a great deal of research in this direction, which has led to the development of publicly available datasets which have been processed, segmented, and labeled by medical experts. This work proposes an ensemble composed of a variety of classifiers suitably adapted to the present problem. Such classifiers are based on a standardized feature extraction front-end representing the involved audio signals limiting the necessity to design handcrafted features. In addition, we elaborate on a prearranged publicly available dataset and introduce an experimental protocol taking into account model bias originating from subject dependency. After thorough experiments, the proposed model was able to outperform the state of the art both in patient-dependent and -independent settings.