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The global spread of COVID-19 had severe consequences for public health andthe world economy. The quick onset of the pandemic highlighted the potentialbenefits of cheap and deployable pre-screening methods to monitor theprevalence of the disease in a population. Various researchers made use ofmachine learning methods in an attempt to detect COVID-19. The solutionsleverage various input features, such as CT scans or cough audio signals, withstate-of-the-art results arising from deep neural network architectures.However, larger models require more compute; a pertinent consideration whendeploying to the edge. To address this, we first recreated two models that usecough audio recordings to detect COVID-19. Through applying network pruning andquantisation, we were able to compress these two architectures without reducingthe model's predictive performance. Specifically, we were able to achieve an105.76x and an 19.34x reduction in the compressed model file size withcorresponding 1.37x and 1.71x reductions in the inference times of the twomodels.

EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection