CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning

• URL: http://arxiv.org/abs/2508.03764v1
• Date: Mon, 04 Aug 2025 23:09:07 GMT
• Title: CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning
• Authors: Justin Luong, Hao Xue, Flora D. Salim,
• Abstract summary: CoughViT is a novel pre-training framework for learning general-purpose cough sound representations.<n>We employ masked data modelling to train a feature encoder in a self-supervised learning manner.<n> Experimental results show that our representations match or exceed current state-of-the-art supervised audio representations.
• Score: 8.789624590579903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physicians routinely assess respiratory sounds during the diagnostic process, providing insight into the condition of a patient's airways. In recent years, AI-based diagnostic systems operating on respiratory sounds, have demonstrated success in respiratory disease detection. These systems represent a crucial advancement in early and accessible diagnosis which is essential for timely treatment. However, label and data scarcity remain key challenges, especially for conditions beyond COVID-19, limiting diagnostic performance and reliable evaluation. In this paper, we propose CoughViT, a novel pre-training framework for learning general-purpose cough sound representations, to enhance diagnostic performance in tasks with limited data. To address label scarcity, we employ masked data modelling to train a feature encoder in a self-supervised learning manner. We evaluate our approach against other pre-training strategies on three diagnostically important cough classification tasks. Experimental results show that our representations match or exceed current state-of-the-art supervised audio representations in enhancing performance on downstream tasks.

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