Browse > Article
http://dx.doi.org/10.6109/jkiice.2011.15.7.1487

Multiple octave-band based genre classification algorithm for music recommendation  

Lim, Shin-Cheol (세종대학교)
Jang, Sei-Jin (전자부품연구원 디지털미디어연구센터)
Lee, Seok-Pil (전자부품연구원 디지털미디어연구센터)
Kim, Moo-Young (세종대학교 정보통신공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.15, no.7, 2011 , pp. 1487-1494 More about this Journal
Abstract
In this paper, a novel genre classification algorithm is proposed for music recommendation system. Especially, to improve the classification accuracy, the band-pass filter for octave-based spectral contrast (OSC) feature is designed considering the psycho-acoustic model and actual frequency range of musical instruments. The GTZAN database including 10 genres was used for 10-fold cross validation experiments. The proposed multiple-octave based OSC produces better accuracy by 2.26% compared with the conventional OSC. The combined feature vector based on the proposed OSC and mel-frequency cepstral coefficient (MFCC) gives even better accuracy.
Keywords
Music genre classification; music recommendation; MFCC; OSC; Texture window;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 G. Tzanetakis and P. Cook, "Musical genre classification of audio signals," IEEE Trans. Speech Audio Process., vol. 10, no. 5, pp. 293-302, 2002.   DOI   ScienceOn
2 D. N. Jiang, L. Lu, H. J. Zhang, J. H. Tao, and L. H. Cai, "Music type classification by spectral contrast feature," in Proc. IEEE Int. Conf. Multimedia and Expo, vol. 1, pp. 113-116, 2002.
3 K. K. Chang, J-S. R. Jang, and C. S. Iliopoulos, "Music Genre Classification via Compressive Sampling," Int. Society for Music Information Retrieval Conf., pp. 387-392, 2010.
4 김창근, 박진영, 박정원, 이광석, 허강인, "독립성분 분석을 이용한 DSP 기반의 화자 독립 음성 인식 시스템의 구현," 한국해양정보통신학회논문지, no. 8, vol. 2, pp. 359-364, 2005.
5 박진영, 이광석, 고시영, 허강인, "잡음환경에서의 음성인식을 위한 변이특성을 고려한 파라메터," 한국해양정보통신학회, 학술대회논문집, pp. 469-472, 2005.
6 The range of a musical instrument : "http://en.wikipedia.org/wiki/Range_(music)"
7 Y. Wang, "A Tree-Based Multi-class SVM Classifier for Digital Library Document," in Proc. IEEE Int. Conf. Multimedia and Information Technology, pp. 15-18, 2008.
8 이금분, 조범준, "다중 클래스 SVM을 이용한 EMD 기반의 부정맥 신호 분류," 한국해양정보통신학회, 한국해양정보통신학회논문지, no. 14, vol. 1, pp. 16-22, 2010.
9 한학용, "패턴인식 개론 : MATLAB 실습을 통한 입체적 학습," 2005.
10 GTZAN Genre Collection Database, "http://marsyas.info/download/data_sets"
11 http://www.melon.com
12 http://bugs.co.kr
13 B. Shao, D. Wang, T. Li, and M. Ogihara, "Music Recommendation Based on Acoustic Features and User Access Patterns," IEEE Trans. Speech Audio Process., vol. 17, no. 8, pp. 1602-1611, 2009.   DOI   ScienceOn
14 L. Cao and M. Guo, "Consistent Music Recommendation in Heterogeneous Pervasive Environment," IEEE Int. Symposium on Parallel and Distributed Process. with Applications, pp. 495-501, 2008.
15 D-M. Kim, K-S. Kim, K-H. Park, J-H. Lee and K. M. Lee, "A music recommendation system with a dynamic k-means clustering algorithm," Int. Conf. Machine Learning and Applications, pp. 399-403, 2007.
16 X. Z.hu, Y-Y. Shi, H-G. Kim, and K-W. Eom, "An integrated music recommendation system," IEEE Trans. Consumer Electronics, vol. 52, no. 3, pp. 917-925, 2006.   DOI   ScienceOn
17 N. Scaringella, G. Zoia, and D. Mlynek, "Automatic genre classification of music content: A survey," IEEE Signal Process., vol. 23, no. 2, pp. 133-141, 2006.