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arXiv · 2606.21411

CoughPhase-CLR: Designing an acoustics-informed foundation model for coughing sound classification

Marius Moldovan·Anton Batliner·Thomas M. Berghaus·Björn W. Schuller·Andreas Triantafyllopoulos
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Abstract

In this work, we introduce CoughPhase-CLR, a self-supervised learning framework designed to leverage the physiological phases of a cough for robust representation learning. Unlike generic contrastive frameworks, CoughPhase-CLR constructs positive pairs based on these specific acoustic phases. We pre-trained our model on approximately 40 hours of public cough audio and evaluated it across five downstream tasks, including COVID-19 detection, chronic obstructive pulmonary disease (COPD) state classification, and smoker status prediction. Our results demonstrate that cough-specific pre-training consistently outperforms standard random-cropping techniques when training on cough recordings. Additionally, we benchmarked a diverse set of state-of-the-art models on COPD state classification, highlighting the difficulty of this task. The best-performing models, pretrained on either general audio or respiratory sounds, achieved a UAR of 57\%, failing to outperform the state-of-the-art performance of 84\% UAR achieved using speech analysis.

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Marius Moldovan, Anton Batliner, Thomas M. Berghaus, Björn W. Schuller, Andreas Triantafyllopoulos. 2026-06-19. CoughPhase-CLR: Designing an acoustics-informed foundation model for coughing sound classification. https://arxiv.org/abs/2606.21411

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