Combining deep and hand-crafted features for audio-based pain intensity classification

Abstract

In this work, the classification of pain intensity based on recorded breathing sounds is addressed. A classification approach is proposed and assessed, based on hand-crafted features and spectrograms extracted from the audio recordings. The goal is to use a combination of feature learning (based on deep neural networks) and feature engineering (based on expert knowledge) in order to improve the performance of the classification system. The assessment is performed on the SenseEmotion Database and the experimental results point to the relevance of such a classification approach.