Fuzzy Rule Based System Ensemble for Music Genre Classification

  • Francisco Fernández
  • Francisco Chávez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7247)


Automatic music retrieval processes rely on classification and tagging systems. Among the tags usually employed for classifying music, genre is a prominent one. This paper presents an ensemble of classifiers that uses a hybrid genetic fuzzy approach. By using a set of Fuzzy Rule Based Systems automatically tuned by means of a Genetic Algorithm, and structured in two layers, the system is capable of correctly classifying classical and jazz samples randomly chosen from a wide set of authors and styles.

The ensemble is built on top of a previously developed method that profits from non-precise information by using Fuzzy Systems. The inherently ambiguous information frequently related to music genre is properly managed by a Fuzzy Rule Based System that focuses on random samples extracted from the audio to be analyzed. A set of these Fuzzy Rule Based Systems are then applied simultaneously to a number of samples, and the final system is in charge of processing the partial information obtained by each of the Fuzzy Rule Based System.

The experimental setup and results take into account harmonic principles and their relationship with the specific genre considered. The system is capable of providing good classification accuracy by using an extremely narrow set of features.


Fuzzy Rule Test Step Classical Music Training Step Music Genre 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alcalá, R., Alcalá-Fdez, J., Herrera, F.: A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Transactions on Fuzzy Systems 15(4), 616–635 (2007)CrossRefGoogle Scholar
  2. 2.
    Alcalá, R., Alcalá-Fdez, J., Gacto, M.J., Herrera, F.: Improving fuzzy logic controllers obtained by experts: A case study in hvac systems. Applied Intelligence 31(1), 15–30 (2009)CrossRefGoogle Scholar
  3. 3.
    Alcalá, R., Alcalá-Fdez, J., Gacto, M.J., Herrera, F.: Rule base reduction and genetic tuning of fuzzy systems based on the linguistic 3-tuples representation. Soft Computing 11(5), 401–419 (2007)CrossRefGoogle Scholar
  4. 4.
    Alcalá, R., Benítez, J., Casillas, J., Cordón, O., Pérez, R.: Fuzzy control of hvac systems optimized by genetic algorithms. Applied Intelligence 18(2), 155–177 (2003)zbMATHCrossRefGoogle Scholar
  5. 5.
    Cordón, O., Gomide, F.A.C., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: GENETIC FUZZY SYSTEMS. Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. World Scientific, New York (2001)zbMATHGoogle Scholar
  7. 7.
    Daniel, F.P., Cazaly, D.: A taxonomy of musical genres. In: Proc. Content-Based Multimedia Information Access, RIAO (2000)Google Scholar
  8. 8.
    Dannenberg, R., Foote, J., Tzanetakis, G., Weare, C.: Panel: new directions in music information retrieval. In: Proc. Int. Computer Music Conference (2001)Google Scholar
  9. 9.
    Fernández, F., Chávez, F.: On the application of Fuzzy Rule-Based Systems to Musical Genre Classification. In: 1st Workshop in Evolutionary Music. IEEE CEC, New Orleans, EE.UU, pp. 25–31 (2011)Google Scholar
  10. 10.
    Fernández, F., Chávez, F., Alcalá, R., Herrera, F.: Musical Genre Classification by means of Fuzzy Rule-Based Systems: A preliminary approach. In: IEEE Congress on Evolutionary Computation, IEEE CEC, New Orleans, EE.UU, pp. 2571–2577 (2011)Google Scholar
  11. 11.
    Herrera, F.: Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence 1(1), 27–46 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Herrera, F., Lozano, M., Verdegay, J.L.: Tuning fuzzy logic controllers by genetic algorithms. International Journal of Approximate Reasoning 12(3), 299–315 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Laroche, J.: Estimating tempo, swing and beat locations in audio recordings. In: 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics, pp. 135–138 (2001)Google Scholar
  14. 14.
    Li, G., Khokhar, A.A.: Content-based indexing and retrieval of audio data using wavelets. In: IEEE International Conference on Multimedia and Expo (II), pp. 885–888 (2000)Google Scholar
  15. 15.
    Li, T., Ogihara, M.: Music Genre Classification with taxonomy. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), pp. 197–200 (2005)Google Scholar
  16. 16.
    Scaringella, N., Zoia, G., Mlynek, D.: Automatic genre classification of music content: A survey. IEEE Signal Processing Magazine 23(2), 133–141 (2006)CrossRefGoogle Scholar
  17. 17.
    Silla, C.N., Koerich, A.L., Kaestner, C.A.A.: Feature selection in automatic music genre classification. In: International Symposium on Multimedia, pp. 39–44 (2008)Google Scholar
  18. 18.
    Silla, C.N., Koerich, A.L., Kaestner, C.A.A.: A machine learning approach to automatic music genre classification. Journal of the Brazilian Computer Society 14, 7–18 (2008)CrossRefGoogle Scholar
  19. 19.
    Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)CrossRefGoogle Scholar
  20. 20.
    Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst., Man, Cybern. 22(6), 1414–1427 (1992)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wold, E., Blum, T., Keislar, D., Wheaten, J.: Content-based classification, search, and retrieval of audio. IEEE MultimediaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Fernández
    • 1
  • Francisco Chávez
    • 1
  1. 1.Department of Computer ScienceUniversity of ExtremaduraMéridaSpain

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