Music Style Classification with a Novel Bayesian Model

  • Yatong Zhou
  • Taiyi Zhang
  • Jiancheng Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Music style classification by mean of computers is very useful to music indexing, content-based music retrieval and other multimedia applications. This paper presents a new method for music style classification with a novel Bayesian-inference-based decision tree (BDT) model. A database of total 320 music staffs collected from CDs and the Internet is used for the experiment. For classification three features including the number of sharp octave (NSO), the number of simple meters (NSM), and the music playing speed (MPS) are extracted. Following that, acomparative evaluation between BDT and traditional decision tree (DT) model is carried out on the database. The results show that the classification accuracy rate of BDT far superior to existing DT model.


Bayesian Model Decision Tree Model Classification Framework Classification Accuracy Rate Music Style 
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  1. 1.
    Qin, D., Ma, G.Z.: Music style identification system based on mining technology. Computer Engineering and Design 26, 3094–3096 (2005)Google Scholar
  2. 2.
    Ma, G.Z., Qin, D.: Music style classification using mutual information (in Chinese). Computer Applications 25, 1116–1118 (2005)Google Scholar
  3. 3.
    Kuo, F.F., Shan, M.K.: A personalized music filtering system based on melody style classification. In: Blum (ed.) Proc. IEEE Int. Conf. Data Mining, pp. 649–652 (2002)Google Scholar
  4. 4.
    Hsu, J., Lin, C., Chen, A.L.: Discovering Nontrivial Repeating Patterns in Music Data. IEEE Trans. Multimedia 3, 311–325 (2001)CrossRefGoogle Scholar
  5. 5.
    Zhang, Y.B., Zhou, J.: A study on content-based music classification. In: Jordan (ed.) Proc. 7th Int. Sym. Signal Processing and Its Applications, vol. 2, pp. 113–116. Paris, France (2003)Google Scholar
  6. 6.
    Word, E., Blum, T., Keislar, D.: Content-Based Classification, Search, and Retrieval of Audio. IEEE Trans. MultiMedia 3, 27–36 (1996)CrossRefGoogle Scholar
  7. 7.
    Xu, C.S., Maddage, N.C., Shao, X.: Automatic music classification and summarization. IEEE Trans. Speech and Audio Processing 3, 441–450 (2005)Google Scholar
  8. 8.
    Lee, S.K.: On generalized multivariate decision tree by using GEE. Computational Statistics & Data Analysis 49, 1105–1119 (2005)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Denson, D.G.T.: Simulation based Bayesian nonparametric regression methods. Ph.D Dissertation. Imperial College, London University (2001)Google Scholar
  10. 10.
    Green, P.J.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yatong Zhou
    • 1
  • Taiyi Zhang
    • 1
  • Jiancheng Sun
    • 2
  1. 1.Dept. Information and Communication EngineeringXi’an Jiaotong UniversityXi’anP.R. China
  2. 2.Dept. Communication EngineeringJiangxi University of Finance and EconomicsNachangP.R. China

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