Abstract
With the increased amount of music that is available to the average user, either online or through their own collection, there is a need to develop new ways to organize and retrieve music. We propose a system by which we develop a set of personalized emotion classifiers, one for each emotion in a set of 16 and a set unique to each user. We train a set of emotion classifiers using feature data extracted from audio which has been tagged with a set of emotions by volunteers. We then develop SVM, kNN, Random Forest, and C4.5 tree based classifiers for each emotion and determine the best classification algorithm. We then compare our personalized emotion classifiers to a set of non-personalized classifiers. Finally, we present a method for efficiently developing personalized classifiers based on hierarchical clustering.
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References
Lartillot, O., Toiviainen, P., Eerola, T.: MIRtoolbox. University of Jyväskylä (2008)
Laurier, C., Herrera, P.: Automatic detection of emotion in music: interaction with emotionally sensitive machines. In: Vallverdu, D., Casacuberta, D. (eds.) Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, pp. 9–32. IGI Global, Hershey (2009)
Yang, Y.H., Su, Y.F., Lin, Y.C., Chen, H.H.: Music emotion recognition: the role of individuality. In: Proceedings of International Workshop on Human-Centered Multimedia 2007 (HCM’07), Augsburg, Germany, September 2007. ACM (2007)
Lu, C.C., Tseng, V.S.: A novel method for personalized music recommendation. Expert Syst. Appl. 36(6), 10035–10044 (2009)
Grekow, J., Raś, Z.W.: Detecting emotions in classical music from MIDI files. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 261–270. Springer, Heidelberg (2009)
Mckay, C., Fujinaga, I.: Automatic genre classification using large high-level musical feature sets. In: ISMIR 2004, pp. 525–530 (2004)
Silla Jr, C.N., Koerich, A.L., Kaestner, C.A.A.: A machine learning approach to automatic music genre classification. J. Braz. Comp. Soc. 14(3), 7–18 (2008)
Kursa, M., Rudnicki, W., Wieczorkowska, A., Kubera, E., Kubik-Komar, A.: Musical instruments in random forest. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 281–290. Springer, Heidelberg (2009)
Laurier, C., Meyers, O., Marxer, R., Bogdanov, D., Serrà, J., Gómez, E., Herrera, P., Wack, N.: Music classification using high-level models. In: ISMIR 2009 (2010)
Mandel, M.I., Ellis, D.P.W.: Song-level features and support vector machines for music classification. In: Reiss, J.D., Wiggins, G.A. (eds.) ISMIR 2005, London, U.K., vol. 6, pp. 594–599. (2005)
Panda, R., Paiva, R.P.: Using support vector machines for automatic mood tracking in audio music. In: 130th Audio Engineering Society Convention (2011)
Zhang, X., Ras, Z.: Differentiated harmonic feature analysis on music information retrieval for instrument recognition. In: IEEE GrC 2006, Atlanta, Georgia, pp. 578–581 (2006)
Eerola, T., Vuoskoski, J.K.: A comparison of the discrete and dimensional models of emotion in music. Psychol. Music 39(1), 18–49 (2011)
McNair, D.M., Lorr, M., Droppleman, L.F.: Profile of Mood States (POMS) (1971)
Thayer, R.E.: The Biopsychology of Mood and Arousal. Oxford University Press, New York (1989)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. ACM SIGKDD Explor. Newslett. 11(1), 10 (2009)
Michalski, R., Mozetic, I., Hong, J., Lavarac, N.: The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In: AAAI-86 Proceedings, pp. 1041–1045. AAAI (1986)
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This project was partially supported by the Research Center of PJIIT, supported by the Polish Ministry of Science and Higher Education.
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Mostafavi, A.C., Raś, Z.W., Wieczorkowska, A.A. (2014). From Personalized to Hierarchically Structured Classifiers for Retrieving Music by Mood. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_15
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DOI: https://doi.org/10.1007/978-3-319-08407-7_15
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