Two-Level Hierarchical Classification of Music Genre for Music Social Networks

• Autorzy: Dropik Ł. , Łukasik E.
• Języki publikacji: EN

Abstrakty

EN

The paper deals with the problem of the content based categorization of music and is focused on a music genre classification. The music genre is a set of characteristics of a group of musical works which distinguishes a given piece from the others. This subject is especially important in social networks, where users are interested in creating playlists or to benefit from efficient recommendation systems. An open source simple and robust system with two-level hierarchical taxonomy is proposed that would facilitate the genre classification of popular music published in free social networks by independent artists. As a test bed for experiments the music published in the free music service Jamendo.com has been used.

Słowa kluczowe

EN

music genre classification; music retrieval; social networks

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2010
• Tom: Vol. 35, No. 4
• Strony: 261--282

Twórcy

autor

Dropik Ł.

autor

Łukasik E.

• institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland,

Bibliografia

• [1] AllMusic. http://en.wikipedia.org/wiki/Allmusic.
• [2] DeCoro C., Barutcuoglu Z., and Fiebrink R. Bayesian aggregation for hierarchical genre classification. In ISMIR 2007 conference proceedings, pages 77-80, 2007.
• [3] McKay C. and Fujinaga I. Combining features extracted from audio, symbolic and cultural sources. In Proc. ISMIR 2008, pages 597-602, 2008.
• [4] Platt J. C. Fast Training of Support Vector Machines using Sequential Minimal Optimization. In Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, 1999.
• [5] Creative Commons, http: //creativecommons.org/.
• [6] Duff D. Modern Genre Theory. Longman, 2000.
• [7] Ogg documentation, http: //xiph.org/ogg/doc.
• [8] Kurth F., Ghermann T., and Muller M. The Cyclic Beat Spectrum: Tempo-Related Audio Features for Time-Scale Invariant Audio Identification. In Proceedings of the Seventh International Conference on Music Information Retrieval (ISMIR), 2006.
• [9] Pachet F. and Cazaly D. A Taxonomy of Musical Genres. In Content-Based Multimedia Information Access Conference (RIAO), Paris, 2000.
• [10] Pachet F. and Aucouturier J. Representing Musical Genre: A State of the Art. Journal of New Music Research, pages 83-93, 2003.
• [11] FreeDB. www.freedb.org.
• [12] Tzanetakis G. and Cook P. Musical genre classification of audio signals. IEEE Trans. Speech Audio Processing, 10:293-302, 2002.
• [13] Witten I. H. and Frank E. WEKA: Machine Learning Algorithms in Java. In Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.
• [14] Ben-Gal I. Bayesian Network. In Encyclopedia of Statistics in Quality and Reliability. John Wiley & Sons, 2007.
• [15] ISO/IEC FCD 15938-4 Information Technology - Multimedia Content Description Interface - Part 4 Audio.
• [16] Abesser J., Lukashevich H., Dittmar Ch., and Schuller G. Genre Classification Using Bass-Related High-Level Featues and Playing Styles. In ISMIR 2009 conference proceedings, pages 453-458, 2009.
• [17] Bergstra J., Lacoste A., and Eck D. Predicting genre labels for artists using FreeDB. In ISMIR 2006 conference proceedings, 2006.
• [18] Jamendo. http://www.jamendo.com.
• [19] Burred J.J. and Lerch A. A hierarchical approach to automatic musical genre classification. In Proc. DA Fx 2003, pages 8-11, 2003.
• [20] Silla C. N. Jr., Koerich A. L., and Kaestner C.A.A. Improving Automatic Music Genre Classification With Hybrid Content-Based Feature Vectors. In ACM SAC 2010, pages 1702-1707. Sierre, 2010.
• [21] Grant K. Film Genre Reader III. Univ. of Texas Press, 2003.
• [22] Harasymowicz K. Automatic recognition of musical genre (in polish). Master's thesis, Poznan University of Technology, Poznań, 2007.
• [23] Kilkki K. Peer to Peer music consumption, unpublished essay. http://kilkki.net, 2008.
• [24] Last.fm. http://www.last.fm.
• [25] J.H. Lee and J.S. Downie. Survey of Music Information Needs, Uses and Seeking Behaviours, a Preliminary Findings. In Proc. ISMIR 2004, 2004.
• [26] Frigo M. and Johnson S.G. The Design and Implementation of FFTW3. In Proc. IEEE 93 no 2, pages 216-231, 2005.
• [27] Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., and Witten I.H. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11, 2009.
• [28] Marsyas. http: //marsyas. sourgeforge .net.
• [29] MIREX contest results. http://www.music-ir.org/evaluation/mirex-results.
• [30] Lamere P. Social tagging and music information retrieval. Journal of New Music Research, pages 101-114, 2008. Special issue: From genres to tags: Music Information Retrieval in the era of folksonomies.
• [31] Petruncio D. S. Evaluation of Various Features for Music Genre Classification with Hidden Markov Models. Master's thesis, University of Illinois, 2002.
• [32] Sigurdsson S., Petersen K. B., and Lehn-Schioler T. Mel Frequency Cepstral Coefficients: An Evaluation of Robustness of MP3 Encoded Music. In Proceedings of the Seventh International Conference on Music Information Retrieval (ISMIR), 2006.
• [33] Sundaram S. and Narayanan S. Experiments in Automatic Genre Classification of Full-length Music Tracks using Audio Activity Rate. In Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop, pages 98-102, 2007.
• [34] Heittola T. Automatic classification of music signals. Master's thesis, Tampere University of Technology, 2003.
• [35] Kamm T., Hermansky H., and Andreou A. G. Learning the Mel scale and Optimal VTN Mapping. Technical report, WS97, 1997.
• [36] Hsu Ch. W., Chang Ch.Ch., and Lin Ch.J. A practical guide to support vector classification. Technical report, National Taiwan University, 2008.
• [37] Wikipedia. ww.en.wikipedia.org.
• [38] Feng Y., Zhuang Y., and Pan Y. Music information retrieval by detecting mood via computational media aesthetics. In Proc. IEEE/WIC Int. Conf. WP2003, pages 235-241, 2003.