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Journal of Intelligent Information Systems

, Volume 52, Issue 1, pp 85–105 | Cite as

Granular methods in automatic music genre classification: a case study

  • Arshia Sathya Ulaganathan
  • Sheela RamannaEmail author
Article
  • 94 Downloads

Abstract

Classification of music files by using the characteristics of the songs based on its genre is a very popular application of machine learning. The focus of this work is on automatic music genre classification based on granular computing methods (fuzzy rough, rough and near sets). We have proposed a modified form of supervised learning algorithm based on tolerance near sets (TCL 2.0) with a goal of exploring the scalability of the learning algorithm to a well researched music database composed of several genres. In the tolerance near set method, tolerance classes are directly induced from the dataset using the tolerance level ε and a distance function. We have compared the tolerance-based near set algorithm to a family of nearest neighbour (NN) algorithms based on fuzzy rough methods (FRNN) available in the WEKA platform. In terms of performance, the classification accuracy of TCL 2.0 is identical to the Bayesian Networks (BN) Algorithm, and comparable with the Sequential Minimal Optimization (SMO) Algorithm. However, the average classification accuracy of FRNN algorithms and the classical rough sets algorithm is better than TCL 2.0, BN and SMO algorithms. For this dataset, any accuracy over 90% is considered a very good classification accuracy which is achieved by all tested classifiers in this work.

Keywords

Granular computing Fuzzy rough sets Machine learning Music genre classification Near sets Rough sets and tolerance near sets 

References

  1. Alusaifeer, T., Ramanna, S., Henry, C. (2013). GPU implementation of MCE approach to finding near neighbourhoods. In Proceedings of the International Conference on Rough Sets and Knowledge Technology (RSKT2013), Lecture Notes in Computer Science (pp. 251–262): Springer.Google Scholar
  2. Banerjee, S., Saha, M., Arun, I., Basak, B., Agarwal, S., Ahmed, R., Chatterjee, S., Mahanta, L. B., Chakraborty, C. (2017). Near-set based mucin segmentation in histopathology images for detecting mucinous carcinoma. Journal of Medical Systems, 41, 144 (2017).  https://doi.org/10.1007/s10916-017-0792-6.
  3. Barbosa, J., McKay, C., Fujinaga, I. (2015). Evaluating automated classification techniques for folk music genres from the brazilian northeast. In Proceedings of 15th Brazilian symposium on computer music, XV (pp. 1–12).Google Scholar
  4. Basili, R., Serafini, A., Stellato, A. (2004). Classification of musical genre: a machine learning approach. In 5th International Society for Music Information Retrieval Conference (ISMIR-2004) (pp. 268–281).Google Scholar
  5. Bazan, J. G., & Szczuka, M. (2005). The Rough Set Exploration System. Springer Transactions on Rough Sets III, LNCS 3400, 37–56.zbMATHGoogle Scholar
  6. Chang, K. K., Jang, J. S. R., Iliopoulos, C. S. (2010). Music genre classification via compressive sampling. In 11th International Society for Music Information Retrieval Conference (ISMIR-2010) (pp. 387–392).Google Scholar
  7. Choi, K., Fazekas, G., Cho, K., Sandler, M. (2017). On the robustness of deep convolutional neural networks for music classification. arXiv:1706.02361.
  8. Cornelis, C., Cock, M. D., Radzikowska, A. M. (2007). Vaguely quantified rough sets. In An, A., Stefanowski, J., Ramanna, S., Butz, C. J., Pedrycz, W., Wang, G. (Eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 11th International Conference, RSFDGrC 2007, Toronto, Canada, May 14-16, 2007. Proceedings (pp. 87–94). Berlin: Springer.Google Scholar
  9. Cornelis, C., De Cock, M., Radzikowska, A. M. (2008). Fuzzy Rough Sets: From Theory into Practice, (pp. 533–552). Hoboken: Wiley.Google Scholar
  10. Costa, Y. M., Oliveira, L. S., Silla, C. N. (2017). An evaluation of convolutional neural networks for music classification using spectrograms. Applied Soft Computing, 52, 28–38.Google Scholar
  11. Dieleman, S., Brakel, P., Schrauwen, B. (2011). Audio-based music classification with a pretrained convolutional network. In 12th International Society for Music Information Retrieval Conference (ISMIR-2011) (pp. 669–674): University of Miami.Google Scholar
  12. Dieleman, S., & Schrauwen, B. (2013). Multiscale approaches to music audio feature learning. In 14th International Society for Music Information Retrieval Conference (ISMIR-2013) (pp. 116–121): Pontifícia Universidade Católica do Paraná.Google Scholar
  13. Doraisamy, S., & Golzari, S. (2010). Automatic Musical Genre Classification and Artificial Immune Recognition System, (pp. 390–402). Berlin: Springer.Google Scholar
  14. Dubois, D., & Prade, H. (1990). Rough fuzzy sets and fuzzy rough sets. International Journal of General System, 17(2-3), 191–209.zbMATHGoogle Scholar
  15. Henaff, M., Jarrett, K., Kavukcuoglu, K., LeCun, Y. (2011). Unsupervised learning of sparse features for scalable audio classification. In 12th International Society for Music Information Retrieval Conference (ISMIR-2011), (Vol. 11 pp. 681–686).Google Scholar
  16. Henry, C. J. (2011). Near Sets: Theory and Applications. Ph.D. thesis, University of Manitoba.Google Scholar
  17. Herrera-Boyer, P., & Gouyon, F. E. (2013). Mirrors: Music information research reflects on its future. Journal of Intelligent Information Systems, 41(3), 1–22.Google Scholar
  18. Hoffmann, P., & Kostek, B. (2014). Music data processing and mining in large databases for active media, (pp. 85–95). Switzerland: Springer International Publishing.Google Scholar
  19. Hoffmann, P., & Kostek, B. (2015). Music genre recognition in the rough set-based environment. In Proceedings of 6th International Conference, PReMI 2015 (pp. 377–386).Google Scholar
  20. Hunt, M., Lennig, M., Mermelstein, P. (1996). Experiments in syllable-based recognition of continuous speech. Proceedings of International Conference on Acoustics, Speech and Signal Processing (pp. 880–883).Google Scholar
  21. Jensen, R., & Cornelis, C. (2011). Fuzzy-rough nearest neighbour classification. Transactions on Rough Sets XIII (56–72).Google Scholar
  22. Keller, J., Gray, M., Givens, J. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transaction on Systems Man Cybernetics, 15(4), 580585.Google Scholar
  23. Khan, M. K., & Wasfi, A. G. (2006). Machine-learning based classification of speech and music. Multimedia Systems, 12(1), 55–67.Google Scholar
  24. Knees, P., & Schedl, M. (2013). A survey of music similarity and recommendation from music context data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 10(1), 2:1–2:21.Google Scholar
  25. Konstantin, M., & Tomoko, M. (2014). Music genre and emotion recognition using gaussian processes. IEEE access, 2, 688–697.Google Scholar
  26. Kostek, B., & Kaczmarek, A. (2013). Music recommendation based on multidimensional description and similarity measures. Fundamenta Informaticae, 50 (1-4), 325–340.Google Scholar
  27. Kostek, B., Hoffmann, P., Kaczmarek, A., Spaleniak, P. (2014). Creating a reliable music discovery and recommendation system. In Intelligent Tools for Building a Scientific Information Platform: From Research to Implementation (pp. 107–130): Springer.Google Scholar
  28. Kostek, B. (2005). Perception-Based Data processing in Acoustics, Applications to Music Information Retrieval and Psychophysiology of Hearing. Series on Cognitive Technologies. Berlin: Springer Verlag.Google Scholar
  29. Logan, B. (2000). Mel frequency cepstral coefficients for music modeling. Plymouth: Proceedings of 1st International Conference on Music Information Retrieval.Google Scholar
  30. Mandel, M. I., & Ellis, D. P. (2008). Multiple-instance learning for music information retrieval. In 9th International Society for Music Information Retrieval Conference (ISMIR-2008) (pp. 577–582).Google Scholar
  31. Marques, C. M., Guilherme, I. R., Nakamura, R. Y., Papa, J. P. (2011). New trends in musical genre classification using optimum-path forest. In 12th International Society for Music Information Retrieval Conference (ISMIR-2011) (pp. 699–704).Google Scholar
  32. Orio, N. (2006). Music retrieval: A tutorial and review. Foundations and Trends®; in Information Retrieval, 1(1), 1–90.zbMATHGoogle Scholar
  33. Panagakis, I., Benetos, E., Kotropoulos, C. (2008). Music genre classification: A multilinear approach. In 9th International Society for Music Information Retrieval Conference (ISMIR-2008) (pp. 583–588).Google Scholar
  34. Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341–356.MathSciNetzbMATHGoogle Scholar
  35. Pawlak, Z., & Skowron, A. (2007). Rudiments of rough sets. Information sciences, 177(1), 3–27.MathSciNetzbMATHGoogle Scholar
  36. Pedrycz, W., Skowron, A., Kreinovich, V. (2008). Handbook of Granular Computing. New York: Wiley-Interscience.Google Scholar
  37. Peters, J. (2007a). Near sets. General theory about nearness of objects. Applied Mathematical Sciences, 1(53), 2609–2029.Google Scholar
  38. Peters, J. (2007b). Near sets. Special theory about nearness of objects. Fundamenta Informaticae, 75(1-4), 407–433.Google Scholar
  39. Peters, J. (2009). Tolerance near sets and image correspondence. International Journal of Bio-Inspired Computation, 1(4), 239–245.Google Scholar
  40. Peters, J. F., & Wasilewski, P. (2009). Foundations of near sets. Information Sciences, 179(18), 3091–3109.MathSciNetzbMATHGoogle Scholar
  41. Peters, J. (2010). Corrigenda and addenda: Tolerance near sets and image correspondence. International Journal of Bio-Inspired Computation, 2(5), 310–318.Google Scholar
  42. Peters, J. F. (2013). Near Sets: An Introduction. Mathematics in Computer Science, 7(1), 3–9.MathSciNetzbMATHGoogle Scholar
  43. Poli, G., Llapa, E., Cecatto, J., Saito, J., Peters, J., Ramanna, S., Nicoletti, M. (2014). Solar flare detection system based on tolerance near sets in a GPU-CUDA framework. Knowledge-Based Systems Journal, Elsevier, 70, 345–360.Google Scholar
  44. Polkowski, L., Skowron, A., Zytkow, J. (1994). Tolerance based rough sets. In Lin, T. Y., & Wildberger, M (Eds.) Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery (pp. 55–58). San Diego: Simulation Councils Inc.Google Scholar
  45. Ramanna, S., & Singh, A. (2016). Tolerance-based approach to audio signal classification. In Proceedings of 29th Canadian AI Conference, LNAI 9673 (pp. 83—88).Google Scholar
  46. Ras, Z., & Wieczorkowska, A. A. (Eds.). (2010). Advances in Music Information Retrieval. Studies in Computational Intelligence, Vol. 274. Switzerland: Springer.Google Scholar
  47. Riza, L. S., Janusz, A., Bergmeir, C., Cornelis, C., Herrera, F., Slezak, D., Benítez, J.M. (2014). Implementing algorithms of rough set theory and fuzzy rough set theory in the r package ”roughsets”. Information Sciences, 287, 68–89.Google Scholar
  48. Rosner, A., & Kostek, B. (2018). Automatic music genre classification based on musical instrument track separation. Journal of Intelligent Information Systems, 50(2), 363–384.Google Scholar
  49. Rough Set Exploration System(RSES). (2005) http://www.mimuw.edu.pl/szczuka/rses/start.html.
  50. Sarkar, M. (2007). Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets and Systems, 158(19), 2134–2152.MathSciNetzbMATHGoogle Scholar
  51. Schedl, M., Gómez, E., Urbano, J. (2014). Music information retrieval: Recent developments and applications. Foundations and trends®; in Information Retrieval, 8 (2-3), 127–261.Google Scholar
  52. Schreiber, H. (2015). Improving genre annotations for the million song dataset. In 16th International Society for Music Information Retrieval Conference (ISMIR-2015) (pp. 241–247).Google Scholar
  53. Silla, C. N., Carlos, N., Koerich, A. L., Kaestner, C. A. A. (2008). The Latin music database. In International Society for Music Information Retrieval Conference (pp. 451–456).Google Scholar
  54. Singh, A. (2017). Application of Tolerance Near Sets to Audio Signal and Commercial Video Classification. Master’s thesis, University of Winnipeg. Supervisor: S.Ramanna.Google Scholar
  55. Singh, A., & Ramanna, S. (2018). Application of tolerance near sets to audio signal classification. In Zielosko, B., Stanczyk, U., Jain, L.C. (Eds.) Advances in Feature Selection, and Data and Pattern Recognition.  https://doi.org/10.1007/978-3-319-67588-613: Springer International Publishing.
  56. Slaney, M., Weinberger, K., White, W. (2008). Learning a metric for music similarity. In International Symposium on Music Information Retrieval (ISMIR).Google Scholar
  57. Sossinsky, A. B. (1986). Tolerance space theory and some applications. Acta Applicandae Mathematica, 5(2), 137–167.MathSciNetGoogle Scholar
  58. Sturm, B. L. (2012). An analysis of the GTZAN music genre dataset. In Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies (pp. 7–12): ACM.Google Scholar
  59. SYNAT Database. (2016) https://synat.eti.pg.gda.pl/.
  60. Thierry, B. M., Ellis, D. P. W., Whitman, B., Lamere, P. (2011). The million song dataset. In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011).Google Scholar
  61. Typke, R., Wiering, F., Veltkamp, R. C. (2005). A survey of music information retrieval systems. In Proceedings of the 6th International Conference on Music Information Retrieval (pp. 153–160). Queen Mary: University of London.Google Scholar
  62. Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.Google Scholar
  63. Wang, X., Yang, J., Teng, X., Peng, N. (2005). Fuzzy-Rough Set Based Nearest Neighbor Clustering Classification Algorithm, (pp. 370–373). Berlin: Springer.Google Scholar
  64. WEKA Data Mining Software System. (2018) http://www.cs.waikato.ac.nz/ml/weka/index.html.
  65. Weston, J., Bengio, S., Hamel, P. (2011). Large-scale music annotation and retrieval: Learning to rank in joint semantic spaces. CoRR arXiv:1105.5196.
  66. Wold, S., Esbensen, K., Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3), 37–52.Google Scholar
  67. Wold, E., Blum, T., Keislar, D., Wheaten, J. (1996). Content-based classification, search, and retrieval of audio. IEEE multimedia, 3(3), 27–36.Google Scholar
  68. Wolski, M. (2010). Perception and classification. a note on near sets and rough sets. Fundamenta Informatica, 101, 143–155.MathSciNetzbMATHGoogle Scholar
  69. Wolski, M. (2013). Granular computing: Topological and categorical aspects of near and rough set approaches to granulation of knowledge. In Transactions on Rough Sets XVI, Lecture Notes in Computer Science, (Vol. 7736 pp. 34–52): Springer Berlin Heidelberg.Google Scholar
  70. Wolski, M., & Gomalínska, A. (2017). Rough and near: modal history of two theories. In Rough Sets: International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3?7, 2017, Proceedings, Part I: Springer International Publishing.Google Scholar
  71. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353.MathSciNetzbMATHGoogle Scholar
  72. Zadeh, L. (1997). Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Systems, 177(19), 111–127.MathSciNetzbMATHGoogle Scholar
  73. Zeeman, E., & Buneman, O. P. (1968). Tolerance spaces and the brain. Towards a Theoretical Biology, 1, 140–151. Published in C.H. Waddington (Ed.), Towards a Theoretical Biology. The Origin of Life, Aldine Pub. Co.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada

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