Music mood classification by rhythm and bass-line unit pattern analysis

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2010, Pages 265-268

  Tsunoo, Emiru ^a   Akase, Taichi ^a   Ono, Nobutaka ^a   Sagayama, Shigeki ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Audio classification; Dynamic programming; Feature extraction; k means clustering; Pattern clustering method

Indexed keywords

AUDIO ACOUSTICS; CLASSIFICATION (OF INFORMATION); EXTRACTION; FEATURE EXTRACTION; MUSIC;

AUDIO CLASSIFICATION; FEATURES EXTRACTION; K-MEANS++ CLUSTERING; LINE PATTERN; MUSIC INFORMATION RETRIEVAL; MUSICAL SCALE; PATTERN ANALYSIS; PATTERN CLUSTERING METHODS; STATISTICAL FEATURE EXTRACTIONS; TEMPORAL FEATURES;

DYNAMIC PROGRAMMING;

EID: 78049392820     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2010.5495964     Document Type: Conference Paper

References (16)

1. "Mirex 2008," http://www.music-ir.org/mirex/2008/.
2. T. Li and M. Ogihara, "Detecting emotion in music," in Proc. of ISMIR, 2003, pp. 239-240.
3. X. Hu, M. Bay, and SJ Downie, "Creating a simplified music mood classification ground-truth set," in Proc. of ISMIR, 2007, pp. 309-310.
4. G. Tzanetakis and P. Cook, "Musical genre classification of audio signals," IEEE Transaction on Speech and Audio Processing, vol. 10, no. 5, pp. 293-302, 2002.
5. M. Mandel, G. Poliner, and D. Ellis, "Support vector machine active learning for music retrieval," ACM Multimedia Systems, vol. 12, no. 1, pp. 3-13, 2006.
6. E. Tsunoo, G. Tzanetakis, N. Ono, and S. Sagayama, "Audio genre classification using percussive pattern clustering combined with timbral features," in Proc. of ICME, 2009, pp. 382-385.
7. E. Tsunoo, N. Ono, and S. Sagayama, "Musical bass-line pattern clustering and its application to audio genre classification," in Proc. of ISMIR, 2009.
8. N. Ono, K. Miyamoto, H. Kameoka, and S. Sagayama, "A real-time equalizer of harmonic and percussive componets in music signals," in Proc. of the 9th Int. Conf. on Music Information Retrieval, September 2008, pp. 139-144.
9. M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, "Rwc music database: Music genre database and musical instrument sound database," in Proc. of the 4th Int. Conf. on Music Information Retrieval, October 2003, pp. 229-230.
10. E. Tsunoo, N. Ono, and S. Sagayama, "Rhythm map: Extraction of unit rhythmic patterns and analysis of rhythmic structure from music acoustic signals," in Proc. of ICASSP, 2009, pp. 185-188.
11. V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, 1995.
12. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman, "Indexing by latent semantic analysis," Journal of the American Society for Information Science, vol. 41, no. 6, pp. 391-407, 1990.
13. B. Logan, "Mel frequency cepstral coefficients for music modeling," in Proc. of ISMIR, 2000.
14. D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, "Towards musical query-by-semantic-description using the cal500 data set," in Proc. of ACM SIGIR, 2007, pp. 439-446.
15. G. Tzanetakis, Marsyas-0.2: A Case Study in Implementing Music Information Retrieval System, chapter 2, pp. 31-49, Idea Group Reference, 2007, Shen, Shepherd, Cui, Liu (eds).
16. I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.