Medical audio classification remains challenging due to low signal-to-noise ratios, subtle discriminative features, and substantial intra-class variability, often compounded by class imbalance and limited training data. Synthetic data augmentation has been proposed as a potential strategy to mitigate these constraints; however, prior studies report inconsistent methodological approaches and mixed empirical results. In this preliminary study, we explore the impact of synthetic augmentation on respiratory sound classification using a baseline deep convolutional neural network trained on a moderately imbalanced dataset (73%:27%). Three generative augmentation strategies (variational autoencoders, generative adversarial networks, and diffusion models) were assessed under controlled experimental conditions. The baseline model without augmentation achieved an F1-score of 0.645. Across individual augmentation strategies, performance gains were not observed, with several configurations demonstrating neutral or degraded classification performance. Only an ensemble of augmented models yielded a modest improvement in F1-score (0.664). These findings suggest that, for medical audio classification, synthetic augmentation may not consistently enhance performance when applied to a standard CNN classifier. Future work should focus on delineating task-specific data characteristics, model-augmentation compatibility, and evaluation frameworks necessary for synthetic augmentation to be effective in medical audio applications.

翻译：医学音频分类由于低信噪比、细微的判别特征以及显著的类内变异性而持续面临挑战，这些问题常因类别不平衡和有限训练数据而加剧。合成数据增强被提出作为缓解这些限制的潜在策略；然而，先前研究报道了不一致的方法论和混合的实证结果。在这项初步研究中，我们使用在中等不平衡数据集（73%:27%）上训练的基准深度卷积神经网络，探讨了合成增强对呼吸音分类的影响。在受控实验条件下评估了三种生成式增强策略（变分自编码器、生成对抗网络和扩散模型）。未使用增强的基准模型获得了0.645的F1分数。在所有单独的增强策略中，均未观察到性能提升，其中若干配置表现出中性或退化的分类性能。仅增强模型的集成获得了适度的F1分数提升（0.664）。这些发现表明，对于医学音频分类，当应用于标准CNN分类器时，合成增强可能无法持续提升性能。未来工作应聚焦于明确任务特定的数据特征、模型与增强的兼容性以及合成增强在医学音频应用中有效所需的评估框架。