Detecting and classifying emotion in popular music

Abstract

Music expresses emotion. However, analyzing the emotion in music by computer is a difficult task. Some work can be found in the literature, but the results are not satisfactory. In this paper, an emotion detection and classification system for pop music is presented. The system extracts feature values from the training music files by PsySound2 and generates a music model from the resulting feature dataset by a classification algorithm. The model is then used to detect the emotion perceived in music clips. To further improve the classification accuracy, we evaluate the significance of each music feature and remove the insignificant features. The system uses a database of 195 music clips to enhance reliability and robustness.