Mood classification of Indian popular music
Aniruddha M. Ujlambkar, Vahida Attar · 2012
Music has been an inherent part of human life when it comes to recreation; entertainment and much recently, even as a therapeutic medium. Music is closely related to human emotions. We often choose to listen to a song or music which best fits our mood at that instant. The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques contributing considerably to analyze and identify the relation of mood with music. We take the same inspiration forward and contribute by making an effort to build a system for automatic identification of mood underlying the audio songs by mining their spectral, temporal audio features. Our current work involves analysis of various classification algorithms in order to learn, train and test the model representing the moods of the audio songs. The focus is on the Indian popular music. The classification model was experimented using a set of 2300 distinct music clips. We have been successful to achieve a satisfactory precision of 70% to 75% in identifying the mood underlying the Indian popular music by introducing the bagging (ensemble) of random forest approach experimented over a total list of 2300 audio clips. We also propose the framework for including lyrics analysis along with audio feature analysis in order to strengthen the accuracy of predicting the mood of an audio file, implementation of which is still under experimentation.