Multimodal mood classification of Hindi and Western songs

Abstract

Music mood classification is one of the most interesting research areas in music information retrieval, and it has many real-world applications. Many experiments have been performed in mood classification or emotion recognition of Western music; however, research on mood classification of Indian music is still at initial stage due to scarcity of digitalized resources. In the present work, a mood taxonomy is proposed for Hindi and Western songs; both audio and lyrics were annotated using the proposed mood taxonomy. Differences in mood were observed during the annotation of the audio and lyrics for Hindi songs only. The detailed studies on mood classification of Hindi and Western music are presented for the requirement of the recommendation system. LibSVM and Feed-forward neural networks have been used to develop mood classification systems based on audio, lyrics, and a combination of them. The multimodal mood classification systems using Feed-forward neural networks for Hindi and Western songs obtained the maximum F-measures of 0.751 and 0.835, respectively.