Cough Classification with Deep Derived Features using Audio Spectrogram Transformer

Cough diagnosis is important for the elderly population, since cough is a key symptom of many respiratory illnesses and conditions. This paper introduces a Transformer-based feature learning approach for the analysis of cough recordings. A Transformer network leveraging feature learning on a big data set is investigated from a feature engineering perspective, in order to find dedicated classification models that can improve overall performance. The latter was achieved through adopting AutoML post-processing techniques on different data sets, driven by the feature engineering process based on both feature selection and feature generation via nonlinear methods. It was found that this approach led to substantial improvements (in the order of 17% from 0.818 to 0.956 of accuracy) on practically all metrics of classification performance, with respect t o t hose obtained with standalone Transformers. Moreover, AutoML models using reduced number of features, either selected or generated, resulted in higher quality models. In particular, a model working only with 1.2 % of the features (nonlinearly generated from the 768 produced by the Transformer), outperformed the model using all of them. These results highlight that big data-derived machine learning models, when post-processed, can play an important role in adapting to small-data scenarios.