Title :
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
fDate :
3/1/2006 12:00:00 AM
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;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2006.1598089
