Music style classification with a novel bayesian model

Authors:
Yatong Zhou;Taiyi Zhang;Jiancheng Sun
Affiliations:
Dept. Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, P.R. China;Dept. Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, P.R. China;Dept. Communication Engineering, Jiangxi University of Finance and Economics, Nachang, P.R. China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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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.