Extraction of Emotional Content from Music Data

This paper presents the system for automatic emotion detection from music data stored in MIDI format files. First, the piece of music is divided into independent segments that potentially represent different emotional states. For this task the method of segmentation is used. The most important part is a features extraction from the music data. On this basis similar emotional parts are grouped by clustering algorithm. Music domain knowledge is used to extract features which are then grouped hierarchically by agglomerative clustering algorithm. Obtained results are visualised by the SOM neural network. The results prove that in the music structure exist features that affect on the human emotion. A novelty of the proposed approach lies in extracted features that discriminate emotional charge of music and application of agglomerative clustering.