@InProceedings{patra-das-bandyopadhyay:2016:COLING,
  author    = {Patra, Braja Gopal  and  Das, Dipankar  and  Bandyopadhyay, Sivaji},
  title     = {Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1980--1989},
  abstract  = {Music information retrieval has emerged as a mainstream research area in the
	past two decades. Experiments on music mood classification have been performed
	mainly on Western music based on audio, lyrics and a combination of both.
	Unfortunately, due to the scarcity of digitalized resources, Indian music fares
	poorly in music mood retrieval research. In this paper, we identified the mood
	taxonomy and prepared multimodal mood annotated datasets for Hindi and Western
	songs. We identified important audio and lyric features using correlation based
	feature selection technique. Finally, we developed mood classification systems
	using Support Vector Machines and Feed Forward Neural Networks based on the
	features collected from audio, lyrics, and a combination of both. The best
	performing multimodal systems achieved F-measures of 75.1 and 83.5 for
	classifying the moods of the Hindi and Western songs respectively using Feed
	Forward Neural Networks. A comparative analysis indicates that the selected
	features work well for mood classification of the Western songs and produces
	better results as compared to the mood classification systems for Hindi songs.},
  url       = {http://aclweb.org/anthology/C16-1186}
}