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：本研究评估了在反映真实世界前瞻性数据采集的小数据集约束下，用于临床环境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比较。我们的方法凸显了在大数据集上预训练后再针对特定临床数据进行微调的优势。我们前瞻性地收集了两个同类首创的中风患者音频数据集。我们研究了多种预处理技术，发现RGB和灰度频谱图变换根据模型从预训练中学习的先验知识对其性能产生不同影响。研究结果表明，在小数据集场景下，CNN模型能够达到甚至超越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增量边际增益的重要性；这为依赖音频分类的临床诊断提供了宝贵见解。