This paper describes our attempt of assessing the capability of music melodies in isolation in order to classify music files into different emotional categories in the context of Sri Lankan music. In our approach, Melodies (predominant pitch sequences) are extracted from songs and the feature vectors are created from them which are ultimately subjected to supervised learning approaches with different classifier algorithms and also with classifier accuracy enhancing algorithms. The models we trained didn’t perform well enough to classify songs into different emotions, but they always showed that the melody is an important factor for the classification. Further experiments with melody features along with some non-melody features showed us that those feature combinations perform much better, hence brought us to the conclusion that, even though, the melody plays a major role in differentiating the emotions into different categories, it needs the support of other features too for a proper classification.
Keywords: Emotion Classification, Ensemble, Feature Selection, Melody, Music Information Retrival, Supervised Learning
