Abstract
We often choose to listen to a song that suits our mood at that instant because an intimate relationship presents between music and human emotions. Thus, the automatic methods are needed to classify music by moods that have gained a lot of momentum in the recent years. It helps in creating library, searching music and other related application. Several studies on Music Information Retrieval (MIR) have also been carried out in recent decades. In the present task, we have built an unsupervised classifier for Hindi music mood classification using different audio related features like rhythm, timber and intensity. The dataset used in our experiment is manually prepared by five annotators and is composed of 250 Hindi music clips of 30 seconds that consist of five mood clusters. The accuracy achieved for music mood classification on the above data is 48%.
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Laurier, C., Grivolla, J., Herrera, P.: Multimodal music mood classification using audio and lyrics. In: Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA 2008), pp. 688–693. IEEE (2008)
Laurier, C., Sordo, M., Herrera, P.: Mood cloud 2.0: Music mood browsing based on social networks. In: Proceedings of the 10th International Society for Music Information Conference (ISMIR 2009), Kobe, Japan (2009)
Liu, D., Lu, L., Zhang, H.J.: Automatic Mood Detection from Acoustic Music Data. In: Proceedings of the International Society for Music Information Retrieval Conference, ISMIR 2003 (2003)
Katayose, H., Imai, H., Inokuchi, S.: Sentiment extraction in music. In: Proceedings of the 9th International Conference on Pattern Recognition, pp. 1083–1087. IEEE (1988)
Russell, J.A.: A Circumplx Model of Affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980)
Bischoff, K., Firan, C.S., Paiu, R., Nejdl, W., Laurier, C., Sordo, M.: Music Mood and Theme Classification-a Hybrid Approach. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), pp. 657–662 (2009)
Lu, L., Liu, D., Zhang, H.J.: Automatic mood detection and tracking of music audio signals. IEEE Transactions on Audio, Speech, and Language Processing 14(1), 5–18 (2006)
Velankar, M.R., Sahasrabuddhe, H.V.: A Pilot Study of Hindustani Music Sentiments. In: Proceedings of 2nd Workshop on Sentiment Analysis Where AI Meets Psychology (COLING 2012), IIT Bombay, Mumbai, India, pp. 91–98 (2012)
Ekman, P.: Facial expression and emotion. American Psychologist 48(4), 384–392 (1993)
Thayer, R.E.: The Biopsychology of Mood and Arousal. Oxford University Press, Oxford (1989)
Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S., Howard, N.: Music Genre Classification: A Semi-supervised Approach. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds.) MCPR 2012. LNCS, vol. 7914, pp. 254–263. Springer, Heidelberg (2013)
Hu, X., Downie, S.J., Laurier, C., Bay, M., Ehmann, A.F.: The 2007 MIREX Audio Mood Classification Task: Lessons Learned. In: Proceedings of the 9th International Society for Music Information Retrieval Conference (ISMIR 2008), pp. 462–467 (2008)
Hu, X., Downie, S.J., Ehmann, A.F.: Lyric text mining in music mood classification. In: Proceedings of 10th International Society for Music Information Retrieval Conference (ISMIR 2009), pp. 411–416 (2009)
Hu, Y., Chen, X., Yang, D.: Lyric-based Song Emotion Detection with Affective Lexicon and Fuzzy Clustering Method. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), pp. 123–128 (2009)
Yang, Y.H., Liu, C.C., Chen, H.H.: Music emotion classification: a fuzzy approach. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 81–84. ACM (2006)
Yang, Y.-H., Lin, Y.-C., Cheng, H.-T., Liao, I.-B., Ho, Y.-C., Chen, H.H.: Toward multi-modal music emotion classification. In: Huang, Y.-M.R., Xu, C., Cheng, K.-S., Yang, J.-F.K., Swamy, M.N.S., Li, S., Ding, J.-W. (eds.) PCM 2008. LNCS, vol. 5353, pp. 70–79. Springer, Heidelberg (2008)
Kim, Y.E., Schmidt, E.M., Migneco, R., Morton, B.G., Richardson, P., Scott, J., Speck, J.A., Turnbull, D.: Music emotion recognition: A state of the art review. In: Proceedings of 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 255–266 (2010)
Fu, Z., Lu, G., Ting, K.M., Zhang, Z.: A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia 13(2), 303–319 (2011)
Ujlambkar, A.M., Attar, V.Z.: Mood classification of Indian popular music. In: Proceedings of the CUBE International Information Technology Conference, pp. 278–283. ACM (2012)
Koduri, G.K., Indurkhya, B.: A Behavioral Study of Emotions in South Indian Classical Music and its Implications in Music Recommendation Systems. In: Proceedings of the 2010 ACM Workshop on Social, Adaptive and Personalized Multimedia Interaction and Access, pp. 55–60. ACM (2010)
Hampiholi, V.: A method for Music Classification based on Perceived Mood Detection for Indian Bollywood Music. World Academy of Science, Engineering and Technology 72, 507–514 (2012)
Patra, B.G., Das, D., Bandyopadhyay, S.: Automatic Music Mood Classification of Hindi Songs. In: Proceedings of 3rd Workshop on Sentiment Analysis where AI meets Psychology (IJCNLP 2013), Nagoya, Japan, pp. 24–28 (2013)
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Patra, B.G., Das, D., Bandyopadhyay, S. (2013). Unsupervised Approach to Hindi Music Mood Classification. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_7
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DOI: https://doi.org/10.1007/978-3-319-03844-5_7
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