# Towards Pre-training an Effective Respiratory Audio Foundation Model

arXiv: https://arxiv.org/abs/2505.15307 · HTML: https://mlsanity.com/paper/2505.15307/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2025.

## Built on by (most cited first, 3 of 3)

- [Beyond Classification: A Cough Regression Benchmark for Respiratory Acoustic Foundation Models](https://mlsanity.com/paper/2606.15436/) (2026)
- [BCoughBench: Benchmarking Respiratory Acoustic Foundation Models Under Body-Coupled Wearable Sensor Conditions](https://mlsanity.com/paper/2606.25116/) (2026)
- [Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment](https://mlsanity.com/paper/2609.00055/) (2026)

## Abstract (arXiv)

Recent advancements in foundation models have sparked interest in respiratory audio foundation models. However, the effectiveness of applying conventional pre-training schemes to datasets that are small-sized and lack diversity has not been sufficiently verified. This study aims to explore better pre-training practices for respiratory sounds by comparing numerous pre-trained audio models. Our investigation reveals that models pre-trained on AudioSet, a general audio dataset, are more effective than the models specifically pre-trained on respiratory sounds. Moreover, combining AudioSet and respiratory sound datasets for further pre-training enhances performance, and preserving the frequency-wise information when aggregating features is vital. Along with more insights found in the experiments, we establish a new state-of-the-art for the OPERA benchmark, contributing to advancing respiratory audio foundation models. Our code is available online at https://github.com/nttcslab/eval-audio-repr/tree/main/plugin/OPERA.