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.
- Files in This Item
-
Go to Link
- Appears in
Collections - ICT융합공학부 > IT공학전공 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.