ABSTRACT
Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.
- E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. In Proc. 17th International Conf. on Machine Learning, pages 9--16. Morgan Kaufmann, San Francisco, CA, 2000.]] Google ScholarDigital Library
- C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.]]Google Scholar
- I. Daubechies. Ten lectures on wavelets. SIAM, Philadelphia, 1992.]] Google ScholarDigital Library
- A. David and S. Panchanathan. Wavelet-histogram method for face recognition. Journal of Electronic Imaging, 9(2):217--225, 2000.]]Google ScholarCross Ref
- H. Deshpande, R. Singh, and U. Nam. Classification of music signals in the visual domain. In Proceedings of the COST-G6 Conference on Digital Audio Effects, 2001.]]Google Scholar
- T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263--286, 1995.]]Google ScholarCross Ref
- W. J. Dowling and D. L. Harwood. Music Cognition. Academic Press, Inc, 1986.]]Google Scholar
- P. Flandrin. Wavelet analysis and synthesis of fractional Brownian motion. IEEE Transactions on Information Theory, 38(2):910--917, 1992.]]Google ScholarDigital Library
- J. Foote. Content-based retrieval of music and audio. In In Multimedia Storage and Archiving Systems II, Proceedings of SPIE, pages 138--147, 1997.]]Google Scholar
- J. Foote and S. Uchihashi. The beat spectrum: a new approach to rhythm analysis. In IEEE International Conference on Multimedia & Expo 2001, 2001.]]Google ScholarCross Ref
- K. Fukunaga. Introduction to statistical pattern recognition. Academic Press, New York, 2nd edition, 1990.]] Google ScholarDigital Library
- G. Fung and O. L. Mangasarian. Multicategory proximal support vector machine classifiers. Technical Report 01-06, University of Wisconsin at Madison, 2001.]]Google Scholar
- M. Goto and Y. Muraoka. A beat tracking system for acoustic signals of music. In ACM Multimedia, pages 365--372, 1994.]] Google ScholarDigital Library
- T. Lambrou, P. Kudumakis, R. Speller, M. Sandler, and A. Linney. Classification of audio signals using statistical features on time and wavelet tranform domains. In Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP-98), volume 6, pages 3621--3624, 1998.]]Google Scholar
- J. Laroche. Estimating tempo, swing and beat locations in audio recordings. In Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA01), 2001.]]Google ScholarCross Ref
- G. Li and A. A. Khokhar. Content-based indexing and retrieval of audio data using wavelets. In IEEE International Conference on Multimedia and Expo (II), pages 885--888, 2000.]]Google Scholar
- T. Li, Q. Li, S. Zhu, and M. Ogihara. A survey on wavelet applications in data mining. SIGKDD Explorations, 4(2):49--68, 2003.]] Google ScholarDigital Library
- B. Logan. Mel frequency cepstral coefficients for music modeling. In Proc. Int. Symposium on Music Information RetrievalISMIR, 2000.]]Google Scholar
- M. K. Mandal, T. Aboulnasr, and S. Panchanathan. Fast wavelet histogram techniques for image indexing. Computer Vision and Image Understanding: CVIU, 75(1--2):99--110, 1999.]]Google Scholar
- Y. Matias, J. S. Vitter, and M. Wang. Wavelet-based histograms for selectivity estimation. In Proceeding of the ACM SIGMOD Conference, pages 448--459, 1998.]] Google ScholarDigital Library
- T. M. Mitchell. Machine Learning. The McGraw-Hill Companies,Inc., 1997.]] Google ScholarDigital Library
- D. Perrot and R. R. Gjerdigen. Scanning the dial: an exploration of factors in the identification of musical style. In Proceedings of the 1999 Society for Music Perception and Cognition, page 88, 1999.]]Google Scholar
- D. Pye. Content-based methods for managing electronic music. In Proceedings of the 2000 IEEE International Conference on Acoustic Speech and Signal Processing, 2000.]] Google ScholarDigital Library
- L. Rabiner and B. Juang. Fundamentals of Speech Recognition. Prentice-Hall, NJ, 1993.]] Google ScholarDigital Library
- J. Saunders. Real-time discrimination of broadcast speech/music. In Proc. ICASSP 96, pages 993--996, 1996.]] Google ScholarDigital Library
- E. Scheirer. Tempo and beat analysis of acoustic musical signals. Journal of the Acoustical Society of America, 103(1), 1998.]]Google ScholarCross Ref
- E. Scheirer and M. Slaney. Construction and evaluation of a robust multifeature speech/music discriminator. In Proc. ICASSP '97, pages 1331--1334, Munich, Germany, 1997.]] Google ScholarDigital Library
- H. Soltau, T. Schultz, and M. Westphal. Recognition of music types. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, 1998.]]Google ScholarCross Ref
- M. Swain and D. Ballard. Color indexing. Int. J. computer vision, 7:11--32, 1991.]] Google ScholarDigital Library
- G. Tzanetakis and P. Cook. Marsyas: A framework for audio analysis. Organized Sound, 4(3):169--175, 2000.]] Google ScholarDigital Library
- G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), July 2002.]]Google ScholarCross Ref
- V. N. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.]]Google ScholarDigital Library
- E. Wold, T. Blum, D. Keislar, and J. Wheaton. Content-based classification, search and retrieval of audio. IEEE Multimedia, 3(2):27--36, 1996.]] Google ScholarDigital Library
- T. Zhang and C.-C. J. Kuo. Audio content analysis for online audiovisual data segmentation and classification. IEEE Transactions on Speech and Audio Processing, 3(4), 2001.]]Google ScholarCross Ref
Index Terms
- A comparative study on content-based music genre classification
Recommendations
Music genre classification using explicit semantic analysis
MIRUM '11: Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategiesMusic genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem ...
Content-based music genre classification using timbral feature vectors and support vector machine
ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and HumanThis paper proposes a novel content-based music genre classification method using timbral feature vectors and support vector machine (SVM). The timbral feature vectors used in the proposed method consist of both the long-term and the short-term features ...
Detecting Musical Genre Borders for Multi-label Genre Classification
ISM '13: Proceedings of the 2013 IEEE International Symposium on MultimediaIn this paper, we propose a novel method to detect music genre borders for the music genre classification. The music genre classification is getting more important because music is influenced by an increasing amount of different musical styles. A ...
Comments