Recognition of emotions in music through the Adaptive-Network-Based Fuzzy (ANFIS)

Abstract

This study aims to recognise emotions in music through the Adaptive-Network-Based Fuzzy (ANFIS). For this, we applied such structure in 877 MP3 files with thirty seconds duration each, collected directly on the YouTube platform, which represent the emotions anger, fear, happiness, sadness, and surprise. We developed four classification strategies, consisting of sets of five, four, three, and two emotions. The results were considered promising, especially for three and two emotions, whose highest hit rates were 65.83% for anger, happiness and sadness, and 88.75% for anger and sadness. A reduction in the hit rate was observed when the emotions fear and happiness were in the same set, raising the hypothesis that only the audio content is not enough to distinguish between these emotions. Based on the results, we identified potential in the application of the ANFIS framework for problems with uncertainty and subjectivity.