Evaluating Audio Classifier Performance for Clinical Diagnosis in Settings with Limited Data

This study evaluates the performance of various deep learning models, including CNNs, transformers, and pre-trained audio feature extractors, for audio classification in a clinical setting with limited data. The findings highlight the significance of preprocessing techniques, pre-training on large datasets, and model selection in achieving optimal performance for audio-based clinical diagnostics.