Multi-label Classifier for Emotion Recognition from Music

  • Divya Tomar
  • Sonali Agarwal
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


Music is one of the important medium to express the emotions such as anger, happy, sad, amazed, quiet etc. In this paper, we consider the task of emotion recognition from music as a multi-label classification task because a piece of music may have more than one emotion at the same time. This research work proposes the Binary Relevance (BR) based Least Squares Twin Support Vector Machine (LSTSVM) multi-label classifier for emotion recognition from music. The performance of the proposed classifier is compared with the eight existing multi-label learning methods using fourteen evaluation measures in order to evaluate it from different point of views. The experimental result suggests that the proposed multi-label classifier based emotion recognition system is more efficient and gives satisfactory outcomes over the other existing multi-label classification approaches.


Multi-label classification Emotion recognition Binary relevance Least squares twin support vector machine 


  1. 1.
    Van de Laar, B.: Emotion detection in music, a survey. In: Twente Student Conference on IT. vol. 1, p. 700 (2006)Google Scholar
  2. 2.
    Yang, Y.H., Chen H.H., Machine recognition of music emotion: a review. ACM Trans. Intell. Syst. Technol. (TIST). 3(3), 40 (2012)Google Scholar
  3. 3.
    Yang, Y.H., Lin, Y.C., Su, Y.F. Chen, H.H.: A regression approach to music emotion recognition. IEEE Trans. Audio Speech Lang. Process. 16(2), 448–457 (2008)Google Scholar
  4. 4.
    Tzacheva, A.A., Schlingmann, D., Bell, K.J.: Automatic detection of emotions with music files. Int. J. Soc. Netw. Mining 1(2), 129–140 (2012)CrossRefGoogle Scholar
  5. 5.
    Han, B.J., Ho, S., Dannenberg, R.B., Hwang, E.: SMERS: Music emotion recognition using support vector regression (2009)Google Scholar
  6. 6.
    Li, T., Ogihara, M.: Detecting emotion in music. IISMIR, 3, 239–240 (2003)Google Scholar
  7. 7.
    Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.P.: Multi-label classification of music into emotions. ISMIR 8, 325–330 (2008)Google Scholar
  8. 8.
    Cai, R., Zhang, C., Wang, C., Zhang, L, Ma, W.Y.: Musicsense: contextual music recommendation using emotional allocation modeling. In: Proceedings of the 15th International Conference on Multimedia, 553–556 (2007)Google Scholar
  9. 9.
    Tolos, M., Tato, R., Kemp, T.: Mood-based navigation through large collections of musical data. In: Proceedings of the 2nd IEEE Consumer Communications and Networking Conference (CCNC 2005) pp. 71–75 (2005)Google Scholar
  10. 10.
    Rocha, B., Panda, R., Paiva, R.P.: Music Emotion Recognition: The Importance of Melodic Features. In: 5th International Workshop on Machine Learning and Music, Prague, Czech Republic (2013)Google Scholar
  11. 11.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data: In Data mining and knowledge discovery handbook, pp. 667–685. Springer, US (2010)Google Scholar
  12. 12.
    Bi, W., Kwok, J.: Efficient Multi-label Classification with Many Labels. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 405–413 (2013)Google Scholar
  13. 13.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. (IJDWM) 3(3), 1–13 (2007)CrossRefGoogle Scholar
  14. 14.
    Cortes, C., Vapnik, V.: Support vector network. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  15. 15.
    Vapnik, V.: The nature of statistical Learning, @ndedn. Springer, New York (1998)Google Scholar
  16. 16.
    Chen, D., Odobez, J.M.: Comparison of Support Vector Machine and Neural Network for Text Texture Verification. IDIAP, Switzerland (2002)Google Scholar
  17. 17.
    Tomar, D., Agarwal, S.: A survey on data mining approaches for healthcare. Int. J. Bio-Sci. Bio-Technol. 5(5), 241–266 (2013)CrossRefGoogle Scholar
  18. 18.
    Sweilam, H.N., Tharwat, A.A., Moniem, N.K.A.: Support vector machine for diagnosis cancer disease: a comparative study. Egypt. Inform. J. 11(2), 81–92 (2010)CrossRefGoogle Scholar
  19. 19.
    Agarwal, S., Pandey, G.N.: SVM based context awareness using body area sensor network for pervasive healthcare monitoring. In: Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, pp. 271–278 (2010)Google Scholar
  20. 20.
    Mangasarian, O.L., Wild, E.W.: Multisurface proximal support vector classification via generalized eigenvalues. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 69–74 (2006)Google Scholar
  21. 21.
    Jayadeva, Khemchandani, R., Chandra, S.: Twin Support vector Machine for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 905–910 (2007)Google Scholar
  22. 22.
    Kumar, M.A., Gopal, M.: Least squares twin support vector machines for pattern classification. Expert Syst. Appl. 36, 7535–7543 (2009)CrossRefGoogle Scholar
  23. 23.
    Feng, Y., Zhuang, Y., Pan, Y.: Music information retrieval by detecting mood via computational media aesthetics. In Proceedings of the IEEE/WIC International Conference on Web Intelligence, WI. pp. 235–241 (2003)Google Scholar
  24. 24.
    Lu, L., Liu, D., Zhang, H.J. Automatic mood detection and tracking of music audio signals. IEEE Trans. Audio, Speech, Lang. Process. 14(1), 5–18 (2006)Google Scholar
  25. 25.
    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 Advances in Multimedia Information Processing-PCM 2008, Springer Berlin Heidelberg, pp. 70–79 (2008)Google Scholar
  26. 26.
    Sorower, M.S.: A literature survey on algorithms for multi-label learning. Oregon State University, Corvallis (2010)Google Scholar
  27. 27.
    Zhang, M., Zhou, Z. A review on multi-label learning algorithms. 1–1 (2013)Google Scholar
  28. 28.
    Spyromitros, E., Tsoumakas, G. Vlahavas, I.: An empirical study of lazy multilabel classification algorithms. In Artificial Intelligence: Theories, Models and Applications, Springer Berlin Heidelberg, pp. 401–406 (2008)Google Scholar
  29. 29.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Prajapati, P., Thakkar, A., Ganatra, A.A: Survey and current research challenges in multi-label classification methods. Int. J. Soft Comput. 2Google Scholar
  31. 31.
    Fürnkranz, J., Hüllermeier, E., Mencía, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)CrossRefGoogle Scholar
  32. 32.
    Mencía, E.L., Park, S.H., Fürnkranz. J.: Efficient voting prediction for pairwise multilabel classification. Neurocomput. 73(7), 1164–1176 (2010)Google Scholar
  33. 33.
    Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data: In principles of data mining and knowledge discovery, pp. 42–53. Springer, Berlin (2001)CrossRefGoogle Scholar
  34. 34.
    Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)zbMATHCrossRefGoogle Scholar
  35. 35.
    Luaces, O., Díez, J., Barranquero, J., Coz, J.J.D., Bahamonde, A.: Binary relevance efficacy for multilabel classification. Prog. Artif. Intell. 1(4), 303–313 (2012)CrossRefGoogle Scholar
  36. 36.
    Emotions Dataset. Accessed 20 August 2014

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Indian Institute of Information TechnologyAllahabadIndia

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