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CoughPhase-CLR: Designing an Acoustics-Informed Foundation Model for Coughing Sound Classification

MCML Authors

Link to Profile Björn Schuller

Björn Schuller

Prof. Dr.

Core PI

Health Informatics

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.

misc MBB+26

Preprint

Jun. 2026

Authors

M. Moldovan • A. Batliner • T. M. Berghaus • B. W. Schuller • A. Triantafyllopoulos

Links

arXiv GitHub

Research Area

 B3 | Multimodal Perception

BibTeXKey: MBB+26

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