Multi-objective feature selection in music genre and style recognition tasks

Genetic and Evolutionary Computation Conference, GECCO'11

Volumn , Issue , 2011, Pages 411-418

  Vatolkin, Igor ^a   Preuß, Mike ^a   Rudolph, Günter ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Feature selection; Multi objective optimization of data mining; Music information retrieval

Indexed keywords

AUTOMATED CLASSIFICATION; DIGITAL MUSIC; EVOLUTIONARY APPROACH; EVOLUTIONARY OPTIMIZATION ALGORITHM; FEATURE SETS; GREEDY METHOD; MULTI OBJECTIVE; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE OPTIMIZATION OF DATA MINING; MUSIC CLASSIFICATION; MUSIC GENRE; MUSIC INFORMATION RETRIEVAL; NATURAL FIELDS;

COMPUTER MUSIC; DATA MINING; EVOLUTIONARY ALGORITHMS; INFORMATION RETRIEVAL; MOBILE DEVICES; MULTIOBJECTIVE OPTIMIZATION;

FEATURE EXTRACTION;

EID: 84860399467     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2001576.2001633     Document Type: Conference Paper

References (31)

1. Beume, N., Naujoks, B., and Emmerich, M. (2007). SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653-1669. (Pubitemid 46498552)
2. Bischl, B., Vatolkin, I., and Preuss, M. (2010). Selecting small audio feature sets in music classification by means of asymmetric mutation. In Proc. of the 11th Int'l Conf. on Parallel Problem Solving From Nature (PPSN), pages 314-323. Springer.
3. Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
4. Blume, H., Haller, M., Botteck, M., and Theimer, W. (2008). Perceptual feature based music classification - a dsp perspective for a new type of application. In Proc. of the 8th Int'l Symp. on Systems, Architectures, Modeling and Simulation (SAMOS), pages 92-99.
5. Fiebrink, R. and Fujinaga, I. (2006). Feature selection pitfalls and music classification. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR), pages 340-341.
6. Fremerey, C., Müller, M., and Clausen, M. (2010). Handling repeats and jumps in score-performance synchronization. In Proc. of the 11th Int'l Conf. on Music Information Retrieval (ISMIR), pages 243-248.
7. Fujinaga, I. (1998). Machine recognition of timbre using steady-state tone of acoustic musical instruments. In Proceedings of the International Computer Music Conference (ICMC), pages 207-210. ICMA.
8. Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. (2006). Feature Extraction, Foundations and Applications. Springer.
9. Huang, T., Dagli, C., Rajaram, S., Chang, E., Mandel, M., Poliner, G., and Ellis, D. (2008). Active learning for interactive multimedia retrieval. Proceedings of the IEEE, 96(4):648-667.
10. Jelasity, M., Preuß, M., and Eiben, A. (2007). Operator learning for a problem class in a distributed peer-to-peer environment. In Proc. of the 7th Parallel Problem Solving from Nature (PPSN), pages 172-183.
11. Lartillot, O. and Toiviainen, P. (2007). Mir in matlab (ii): A toolbox for musical feature extraction from audio. In Proc. of the 8th Int'l Conf. on Music Information Retrieval (ISMIR), pages 127-130.
12. Liu, J. and Hu, X. (2010). User-centered music information retrieval evaluation. In Proc. of the Joint Conf. on Digital Libraries (JCDL) Workshop: Music Information Retrieval for the Masses.
13. Lukashevich, H. (2008). Towards quantitative measures of evaluating song segmentation. In Proc. of the 9th Int'l Conf. on Music Inform. Retr. (ISMIR), pages 375-380.
14. Martin, R. and Nagathil, A. (2009). Cepstral Modulation Ratio Regression (CMRARE) Parameters for Audio Signal Analysis and Classification. In Proc. of the Int'l Conf. on Acoustics, Speech and Signal Processing (ICASSP), pages 321-324. IEEE Press.
15. Mierswa, I. (2007). Controlling overfitting with multi-objective support vector machines. In Proc. of the Gen. and Evol. Comput. Conf. (GECCO), pages 1830-1837. ACM. (Pubitemid 47291809)
16. Mierswa, I. and Morik, K. (2005). Automatic feature extraction for classifying audio data. Machine Learning Journal, 58(2-3):127-149. (Pubitemid 40400635)
17. Mugambi, E. and Hunter, A. (2003). Multi-objective genetic programming optimization of decision trees for classifying medical data. In Proc. of the 7th Int'l Conf. on Knowledged-Based Intelligent Information and Engineering Systems (KES), pages 293-299. (Pubitemid 37230687)
18. Müller, M. and Ewert, S. (2010). Towards timbre-invariant audio features for harmony-based music. IEEE Transactions on Audio, Speech, and Language Processing, 18(3):649-662.
19. Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the cuidado project. Technical report, IRCAM, France.
20. Radtke, P., Sabourin, R., and Wong, T. (2009). Solution over-fit control in evolutionary multi-objective optimization of pattern classification systems. Int'l Journal of Pattern Recognition and Artif. Intelligence, 23(6):1107-1127.
21. Ras, Z. and Wieczorkowska, A. (2010). Advances in Music Information Retrieval. Springer, Berlin.
22. Raymer, M., Punch, W., Goodman, E., Kuhn, L., and Jain, A. (2000). Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(2):164-171.
23. Reunanen, J. (2003). Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research, (3):1371-1382.
24. Reynolds, A., Corne, D., and Chantler, M. (2010). Feature selection for multi-purpose predictive models: A many-objective task. In Proc. of the 11th Int'l Conf. on Parallel Problem Solving From Nature (PPSN), pages 384-393. Springer.
25. Seppänen, J., Eronen, A., and Hiipakka, J. (2006). Joint beat and tatum tracking from music signals. In Proc. of the 7th Int'l Conf. on Music Information Retrieval (ISMIR), pages 23-28.
26. Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In Proc. of the AAAI'06 workshop on Evaluation Methods for Machine Learning, pages 1015-1021.
27. Tax, D. (2001). One-Class Classification. PhD thesis, Delft University of Technology, Pattern Recognition Laboratory.
28. Vatolkin, I. (2010). Multi-objective evaluation of music classification. In Proc. of the German Classification Society Conference (GfKl).
29. Vatolkin, I., Theimer, W., and Botteck, M. (2010). Amuse (advanced music explorer) - a multitool framework for music data analysis. In Proc. of the 11th Int'l Society for Music Inform. Retr. Conf. (ISMIR), pages 33-38.
30. Vatolkin, I., Theimer, W., and Rudolph, G. (2009). Design and comparison of different evolution strategies for feature selection and consolidation in music classification. In Proc. of the 2009 IEEE Congress on Evolutionary Computation (CEC), pages 174-181.
31. Zhu, Z., Jia, S., and Ji, Z. (2010). Towards a memetic feature selection paradigm. IEEE Computational Intelligence Magazine, 5(2):41-53.