Automatic genre classification of music content: a survey

Author

Scaringella, Nicolas ; Zoia, Giorgio ; Mlynek, Daniel

Author_Institution

Inst. of Signal Process., Ecole Polytech. Fed. de Lausanne

Volume

23

Issue

2

fYear

2006

fDate

3/1/2006 12:00:00 AM

Firstpage

133

Lastpage

141

Abstract

This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with their definitions and hierarchies. It also presents techniques to extract meaningful information from audio data to characterize musical excerpts. The paper also presents the results of new emerging research fields and techniques that investigate the proximity of music genres

Keywords

audio signal processing; classification; feature extraction; multimedia computing; music; pattern clustering; automatic genre classification; clustering algorithms; expert systems; genre taxonomies; harmony; meaningful information extraction; music collections; music content; music genres; novelty detection; rhythm; similarity measures; supervised classification; timbre; unsupervised classification; Context-aware services; Data mining; Databases; Electronic music; Expert systems; Labeling; Libraries; Multiple signal classification; Search engines; Taxonomy;

fLanguage

English

Journal_Title

Signal Processing Magazine, IEEE

Publisher

ieee

ISSN

1053-5888

Type

jour

DOI

10.1109/MSP.2006.1598089

Filename

1598089