用通用音频数据预训练，能更好提升呼吸音模型效果。
Towards Pre-training an Effective Respiratory Audio Foundation Model
- 用AudioSet通用音频数据预训练，优于专用呼吸音数据
- 融合AudioSet与呼吸音数据进一步提升性能
- 保留频域信息对特征聚合至关重要，适合语音医疗研究者
近年来，基础模型的发展引发了对呼吸音基础模型的兴趣。然而，现有常规预训练方法在小规模、多样性不足的数据集上是否有效尚未充分验证。本研究通过对比多种预训练音频模型，探索更优的呼吸音预训练策略。实验表明，基于AudioSet（通用音频数据集）预训练的模型表现优于专用于呼吸音数据的模型。进一步地，将AudioSet与呼吸音数据联合进行预训练可显著提升性能，且在特征聚合时保持频率维度信息至关重要。实验还揭示了其他关键发现，最终在OPERA基准上达到新最佳水平，推动了呼吸音基础模型的发展。代码已公开于https://github.com/nttcslab/eval-audio-repr/tree/main/plugin/OPERA。
原文摘要 · Abstract (English)
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.
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