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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)

Abstract

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.

Keywords

Bayesian Model Decision Tree Model Classification Framework Classification Accuracy Rate Music Style 
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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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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