The Musical Avatar: A visualization of musical preferences by means of audio content description

Proceedings of the 5th Audio Mostly - A Conference on Interaction With Sound, AM '10

Volumn , Issue , 2010, Pages

  Haro, Martín ^a   Xambó, Anna ^a,b   Fuhrmann, Ferdinand ^a   Bogdanov, Dmitry ^a   Gómez, Emilia ^a   Herrera, Perfecto ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b OPEN UNIVERSITY   (United Kingdom)

Author keywords

audio content analysis; music information research (MIR); music visualization; semantic retrieval; user modeling

Indexed keywords

AUDIO CONTENT ANALYSIS; MUSIC INFORMATION RESEARCH (MIR); MUSIC VISUALIZATION; SEMANTIC RETRIEVAL; USER MODELING;

COMPUTER MUSIC; MATHEMATICAL MODELS; PATTERN RECOGNITION; VISUALIZATION;

AUDIO ACOUSTICS;

EID: 78649440305     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1859799.1859813     Document Type: Conference Paper

References (30)

1. N. Ahmed, E. de Aguiar, C. Theobalt, M. Magnor, and H.-P. Seidel. Automatic generation of personalized human avatars from multi-view video. In Proceedings of the ACM symposium on Virtual reality software and technology, pages 257-260, 2005.
2. J. Aucouturier. Sounds like teen spirit: Computational insights into the grounding of everyday musical terms. In J. Minett and W. Wang, editors, Language, Evolution and the Brain, pages 35-64, 2009.
3. D. Bogdanov, J. Serrà, N. Wack, and P. Herrera. From low-level to high-level: Comparative study of music similarity measures. In International Workshop on Advances in Music Information Research, 2009.
4. P. M. Brossier. Automatic Annotation of Musical Audio for Interactive Applications. PhD thesis, QMUL, London, UK, 2007.
5. O. Celma and X. Serra. Foafing the music: Bridging the semantic gap in music recommendation. Web Semantics: Science, Services and Agents on the World Wide Web, 6(4):250-256, 2008.
6. W. Chai and B. Vercoe. Using user models inmusic information retrieval systems. In International Symposium on Music Information Retrieval, 2000.
7. S. Downie, D. Byrd, and T. Crawford. Ten years of ISMIR: Reflections on challenges and opportunities. Proceedings of the 10th International Society for Music Information Retrieval Conference, pages 13-18, 2009.
8. M. Grimaldi and P. Cunningham. Experimenting with music taste prediction by user profiling. In MIR '04: Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval, pages 173-180, 2004.
9. G. Grimmett. Probability and random processes. Oxford University Press, 3rd edition, 2001.
10. E. Gómez. Tonal Description of Music Audio Signals. PhD thesis, UPF, Barcelona, Spain, 2006.
11. M. A. Hall. Correlation-based feature selection for discrete and numeric class machine learning. In Proceedings of the International Conference on Machine Learning, pages 359-366, 2000.
12. J. Holm, A. Aaltonen, and H. Siirtola. Associating colours with musical genres. Journal of New Music Research, 38(1):87-100, March 2009.
13. C. Huberty. Applied MANOVA and discriminant analysis. Wiley-Interscience, Hoboken N.J., 2nd edition, 2006.
14. C. Laurier, O. Meyers, J. Serrà, M. Blech, P. Herrera, and X. Serra. Indexing music by mood: Design and integration of an automatic content-based annotator. Multimedia Tools and Applications, 2009.
15. D. Levitin. The world in six songs: how the musical brain created human nature. Plume, New York, 2009.
16. M. Maffesoli. The Time of the Tribes: The Decline of Individualism in Mass Society. Sage, 1995.
17. M. Mandel and D. Ellis. Song-level features and support vector machines for music classification. In Proceedings of the 6th International Symposium on Music Information Retrieval, pages 594-599, 2005.
18. S. McCloud. Understanding Comics: The Invisible Art. Kitchen Sink Press, 1993.
19. A. North and D. Hargreaves. Music and adolescent identity. Music Education Research, 1(1):75-92, 1999.
20. A. C. North and D. J. Hargreaves. Lifestyle correlates of musical preference. Psychology of Music, 35(1):58-87, 2007.
21. G. Peeters. A large set of audio features for sound description (similarity and classification) in the CUIDADO project. CUIDADO Project Report, 2004.
22. E. Petajan. Real-Time Vision for Human-Computer Interaction, chapter MPEG-4 Face and Body Animation Coding Applied to HCI. Springer US, 2005.
23. P. Rentfrow and S. Gosling. The Do Re Mi's of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6):1236-1256, 2003.
24. P. Rentfrow and S. Gosling. Message in a ballad. Psychological Science, 17(3):236-242, 2006.
25. D. Sauer and Y.-H. Yang. Music-driven character animation. ACM Transactions Multimedia Computing, Communications and Applications, 5(4):1-16, 2009.
26. S. Stober and A. Nürnberger. User-adaptive music information retrieval. Künstliche Intelligenz, 23(2):54-57, 2009.
27. S. Streich. Music complexity: a multi-faceted description of audio content. PhD thesis, UPF, Barcelona, Spain, 2007.
28. V. Vapnik. The Nature of Statistical Learning Theory (Information Science and Statistics). Springer, 2nd edition, 1999.
29. M. Voong and R. Beale. Music organisation using colour synaesthesia. In CHI '07: extended abstracts on Human factors in computing systems, pages 1869-1874, 2007.
30. C. Xu, N. C. Maddage, X. Shao, F. Cao, and Q. Tian. Musical genre classification using support vector machines. In IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 429-432, 2003.