Publication

Abstract : The amount of information that is available on internet related to music is very huge. The information related to music can be mined using many features, and the on-line contribution of both musical experts and general listeners has provided music researchers with a rich resource of information. The mood of a song helps in recommending songs to online users. Also, there is a strong application-oriented interest in mood classification for music download services and audio players allow music collection browsing using mood as one search criteria. This paper proposes a novel mood classification technique using improved Roccihio algorithm. Since Rocchio algorithm uses only one prototype vector for representing a class, it offers a less prediction accuracy. We addressed this problem by considering the$k$-nearest vectors along with prototype vector. The proposed method is validated using real music data, collected from well known music portals, in comparison with other machine learning methods.