Audio Feature Extraction for Effective Emotion Classification

Recently, there has been increasing interest in artificial intelligence and machine learning, where sentiment analysis has received considerable attention. In several studies, emotional states have been recognized using audio, text, or bio-signals that induce emotions, with audio being the most typical. There are several audio features, such as rhythm, dynamics, melody, harmony, and tonal color. The aim of our paper is finding critical audio features for effective emotion recognition. To do this, we select the existing audio features from elements of music, and investigate critical features using an iterative feature extraction method. For objective evaluation, the International Affective Digital Sounds system was used for training and testing. Crossvalidation evaluated the method in terms of classifier accuracy and computational complexity, and the results indicate the critical features for emotion classification.