This study assesses deep learning models for audio classification in a clinical setting with the constraint of small datasets reflecting real-world prospective data collection. We analyze CNNs, including DenseNet and ConvNeXt, alongside transformer models like ViT, SWIN, and AST, and compare them against pre-trained audio models such as YAMNet and VGGish. Our method highlights the benefits of pre-training on large datasets before fine-tuning on specific clinical data. We prospectively collected two first-of-their-kind patient audio datasets from stroke patients. We investigated various preprocessing techniques, finding that RGB and grayscale spectrogram transformations affect model performance differently based on the priors they learn from pre-training. Our findings indicate CNNs can match or exceed transformer models in small dataset contexts, with DenseNet-Contrastive and AST models showing notable performance. This study highlights the significance of incremental marginal gains through model selection, pre-training, and preprocessing in sound classification; this offers valuable insights for clinical diagnostics that rely on audio classification.

翻译：本研究在反映真实世界前瞻性数据收集的小样本约束下，评估了用于临床环境中音频分类的深度学习模型。我们分析了包括DenseNet和ConvNeXt在内的CNN模型，以及ViT、SWIN和AST等Transformer模型，并将其与YAMNet和VGGish等预训练音频模型进行了比较。我们的方法凸显了在大规模数据集上进行预训练后再针对特定临床数据进行微调的优越性。我们前瞻性收集了两个首批针对中风患者的患者音频数据集。我们研究了多种预处理技术，发现RGB和灰度谱图转换会根据模型从预训练中习得的先验知识以不同方式影响模型性能。研究结果表明，在小样本场景下，CNN能够达到甚至超越Transformer模型的表现，其中DenseNet-Contrastive和AST模型展现出显著性能。本研究强调了在声音分类中通过模型选择、预训练和预处理实现渐进式边际增益的重要性，这为依赖音频分类的临床诊断提供了宝贵见解。