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A study on music genre recognition and classification techniquesopen access

Authors
Nasridinov A.Park Y.-H.
Issue Date
Apr-2014
Publisher
보안공학연구지원센터
Keywords
Chord recognition; Decision tree; Genre classification; Subsequence matching
Citation
International Journal of Multimedia and Ubiquitous Engineering, v.9, no.4, pp 31 - 42
Pages
12
Journal Title
International Journal of Multimedia and Ubiquitous Engineering
Volume
9
Number
4
Start Page
31
End Page
42
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/11028
DOI
10.14257/ijmue.2014.9.4.04
ISSN
1975-0080
Abstract
Automatic classification of music genre is widely studied topic in music information retrieval (MIR) as it is an efficient method to structure and organize the large numbers of music files available on the Internet. Generally, the genre classification process of music has two main steps: feature extraction and classification. The first step obtains audio signal information, while the second one classifies the music into various genres according to extracted features. In this paper, we present a study on techniques for automatic music genre recognition and classification. We first describe machine learning based chord recognition methods, such as hidden Markov models, neural networks, dynamic Bayesian network and rule-based methods, and template matching methods. We then explain supervised, unsupervised and semi-supervised classification methods classifying music genres. Finally, we briefly describe the proposed method for automatic classification of music genres, which consists of three steps: chord labeling, genre matching and classification. © 2014 SERSC.
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