A Cough-based deep learning framework for detecting COVID-19

Abstract

This paper presents a deep learning framework for detecting COVID-19 positive subjects from their cough sounds. In particular, the proposed approach comprises two main steps. In the first step, we generate a feature representing the cough sound by combining an embedding extracted from a pre-trained model and handcrafted features extracted from draw audio recording, referred to as the front-end feature extraction. Then, the combined features are fed into different back-end classification models for detecting COVID-19 positive subjects in the second step. Our experiments on the Track-2 dataset of the Second 2021 DiCOVA Challenge achieved the second top ranking with an AUC score of 81.21 and the top F1 score of 53.21 on a Blind Test set, improving the challenge baseline by 8.43% and 23.4% respectively and showing deployability, robustness and competitiveness with the state-of-the-art systems.

arXiv commentCOVID-19, EMBC-2022, DiCOVA, top 2nd, benchmark on Spec > 0.95%

References in corpus (5)

• Scikit-learn: Machine Learning in Python
• Towards Learning a Universal Non-Semantic Representation of Speech
• Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
• COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
• The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics

Cited by in corpus (1)

• Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses