Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11811305_16 | ADMA |
Keywords | Field | DocType |
acomparative evaluation,music staff,content-based music retrieval,multimedia application,music indexing,novel bayesian model,classification accuracy rate,traditional decision tree,dt model,novel bayesian-inference-based decision tree,music style classification,indexation,decision tree,bayesian inference,bayesian model | Decision tree,Data mining,Octave,Indexation,Bayesian inference,Computer science,Search engine indexing,Decision tree model,Artificial intelligence,Tree structure,Machine learning,The Internet | Conference |
Volume | ISSN | ISBN |
4093 | 0302-9743 | 3-540-37025-0 |
Citations | PageRank | References |
2 | 0.37 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yatong Zhou | 1 | 28 | 5.72 |
| Taiyi Zhang | 2 | 176 | 17.60 |
| Jiancheng Sun | 3 | 43 | 7.79 |