Automatic genre classification of music content

被引：154
|
作者：
Scaringella, N [1 ]
Zoia, G [1 ]
Mlynek, D [1 ]
机构：
[1] Ecole Polytech Fed Lausanne, Signal Proc Inst, Lausanne, Switzerland
来源：
IEEE SIGNAL PROCESSING MAGAZINE | 2006年 / 23卷 / 02期
关键词：
D O I：
10.1109/MSP.2006.1598089
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Music genres are crucial descriptors to organize music catalogues, libraries, and music stores. Despite their use, music genres remain a poorly defined concept. This article reviews the state of the art in automatic genre classification and presents new directions in automatic organization of music collections. It outlines techniques to extract meaningful information from audio data to characterize musical excerpts. It also reviews the state of the art in genre classification through three main paradigms: expert systems, unsupervised classification, and supervised classification.
引用
收藏
页码：133 / 141
页数：9
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