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        <title>Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs</title>
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        <namePart type="given">Braja</namePart>
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs
%A Patra, Braja Gopal
%A Das, Dipankar
%A Bandyopadhyay, Sivaji
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F patra-etal-2016-multimodal
%X 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.
%U https://aclanthology.org/C16-1186
%P 1980-1989

Markdown (Informal)

[Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs](https://aclanthology.org/C16-1186) (Patra et al., COLING 2016)

• Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs (Patra et al., COLING 2016)

ACL

• Braja Gopal Patra, Dipankar Das, and Sivaji Bandyopadhyay. 2016. Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1980–1989, Osaka, Japan. The COLING 2016 Organizing Committee.