Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Techniques on Music Genre Classification
  2.1. Chord Recognition
  2.2. Genre Classification
 3. Proposed Method
  3.1. Chord Labeling Scheme
  3.2. Genre Matching
  3.3. Genre Classification
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Aziz Nasridinov [ School of Computer Engineering, Dongguk University at Gyeongju ]
• Young-Ho Park [ Department of Multimedia Science, Sookmyung Women’s University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605