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

TL;DR

This paper demonstrates how to effectively compress COVID-19 detection models based on cough audio signals using pruning and quantisation, enabling deployment on edge devices without sacrificing accuracy.

Contribution

The authors successfully applied network pruning and quantisation to reduce model size and inference time for audio-based COVID-19 detection models, maintaining their predictive performance.

Findings

Achieved 105.76x and 19.34x reduction in model size

Reduced inference times by 1.37x and 1.71x

Maintained model accuracy after compression

Abstract

The global spread of COVID-19 had severe consequences for public health and the world economy. The quick onset of the pandemic highlighted the potential benefits of cheap and deployable pre-screening methods to monitor the prevalence of the disease in a population. Various researchers made use of machine learning methods in an attempt to detect COVID-19. The solutions leverage various input features, such as CT scans or cough audio signals, with state-of-the-art results arising from deep neural network architectures. However, larger models require more compute; a pertinent consideration when deploying to the edge. To address this, we first recreated two models that use cough audio recordings to detect COVID-19. Through applying network pruning and quantisation, we were able to compress these two architectures without reducing the model's predictive performance. Specifically, we were able to achieve an 105.76x and an 19.34x reduction in the compressed model file size with corresponding 1.37x and 1.71x reductions in the inference times of the two models.