A comprehensive study in benchmarking feature selection and classification approaches for traditional Malay Music genre classification

Proceedings of the 2008 International Conference on Data Mining, DMIN 2008

Volumn , Issue , 2008, Pages 71-77

  Golzari, Shahram ^a   Doraisamy, Shyamala ^a   Norowi, Noris Mohd ^a   Sulaiman, Nasir ^a   Udzir, Nur Izura ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Classification; Data mining; Feature selection; Genre classification

Indexed keywords

AUDIO SIGNALS; CLASSIFICATION; CLASSIFICATION ACCURACIES; COMPREHENSIVE STUDIES; DATA SETS; DIMENSIONALITY REDUCTIONS; FEATURE SELECTION; FEATURE SELECTION AND CLASSIFICATIONS; GENRE CLASSIFICATION; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING TECHNIQUES; MUSIC GENRE CLASSIFICATIONS; MUSICAL GENRE CLASSIFICATIONS; MUSICAL GENRES;

CLASSIFIERS; INFORMATION MANAGEMENT; LEARNING ALGORITHMS; ROBOT LEARNING;

DATA MINING;

EID: 62649168689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. R. Dannenberg, J. Foote, G. Tzanetakis, and C. Weare, "Panel: New Directions in Music Information Retrieval," International Computer Music Conference, International Computer Music Association, pp. 52-59, 2001.
2. G. Tzanetakis and P. Cook, "Musical Genre Classification of Audio Signals," IEEE Transactions on Speech and Audio Processing, vol. 10, no. 5. 2002.
3. E. Wold, T. Blum, D. Keislar, and J. Wheaton, "Content-based Classification, Search, and Retrieval of Audio," IEEE Multimedia, vol.3, no.3, pp. 27-36, 1996.
4. M. G. Nasuruddin, The Malay Traditional Music. Kuala Lumpur: Dewan Bahasa dan Pustaka, 1992.
5. N. Norowi, S. Doraisamy and R.W. Rahmat, Journal of Information Technology in Asia, JITA, Vol. 2, 2007.
6. Y. Saeys, I. Inza, and P. Larranaga, A Review of Feature Selection Techniques in Bioinformatics, Bioinformatics Advance Access, Oxford University press, pgs, Aug. 2007.
7. K. West, and S. Cox, Features and Classifiers for the Automatic Classification of Musical Audio Signals, In Proceedings of the International Conference on Music Information Retrieval, ISMIR 2006.
8. J. Aucoturie and F. Pachet, "Representing Musical Genre: A State of the Art"', Journal of New Music Research. vol. 32, no. 1, pp. 83-93,1993.
9. th International Symposium on Methodologies for Intelligent Systems, pp. 292-300, 2000.
10. K. Kosina, Music Genre Recognition. Diplome Thesis, Hagenberg University, 2002.
11. S. Dixon, "A Beat Tracking System for Audio Analysis," In Proceedings of the Diderot Forum on Mathematics and Music, Austrian Computer Society, pp. 101-110, 1999.
12. T. Li and G. Tzanetakis, "Factors in Automatic Musical Genre Classification of Audio Signals," IEEE Workshop on Applications of Signal Processing and Audio Acoustics, WASPAA 2003, pp. 143-146, 2003.
13. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco,2nd Edition, 2005.
14. I. Kaminskyj, "Multi-feature Musical Instrument Sound Classifier," Mikropolyphonie,WWW Journal, Melbourne, Australia, Vol.6, 2001.
15. S. Lippens, J. Martens, T. Mulder, and G. Tzanetakis, "A Comparison of Human and Automatic Musical Genre Classification," In Proceedings of IEEE International Conference on AcousticSpeech and Signal Processing, Montreal, Canada, 2004.
16. D. Perrot and R. Gjerdigen, "Scanning the Dial: An Exploration of Factors in Identification of Musical Styles," In Proceedings of Society for Music Perception and Cognition. pp. 88, 1999.
17. P. Matusky, Malaysian Shadow Play and Music: Continuity of an Oral Tradition. Kuala Lumpur: Oxford University Press,1993.
18. M. Ang, A Layered Architectural Model for Music: Malaysian Music on the World Wide Web. Ph.D. dissertation: UPM, 1998.
19. J. Becker, "The Percussive Patterns in the Music of Mainland Southeast Asia," Ethnomusicology, vol. 2, no. 2, pp. 173-191,1968.
20. N.M. Norowi, S. Doraisamy, and R. Wirza, "Factors Affecting Automatic Genre Classification: An Investigation Incorporating Non-Western Musical Forms," In Proceedings of the 6th International Conference on Music Information Retrieval, pp. 13-21, 2005.
21. M.A. Hall, and L.A. Smith, "Practical feature subset selection for machine learning", In Proceedings of the 21st Australian Computer Science Conference, pp. 181-191, 1998.
22. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine Learning, Vol.46, pp. 389- 422, 2002.
23. D.E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison-Wesley, 1989.
24. R. Fiebrink and I. Fujinaga, Feature Selection Pitfalls and Music Classification, In Proc. Of the International Conference on Music Information Retrieval, ISMIR 2006, pgs 340-341,2006.
25. A. Watkins, J. Timmis, and L. Boggess. Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Genetic Programming and Evolvable Machines, 5(3):291-317, 2004.
26. J. Brownlee. WEKA Classification Algorithms, Version 1.6, http://sourceforge.net/projects/wekaclassalgos. Accessed December 2007.
27. E. Frank and I. H. Witten. Generating Accurate Rule Sets without Global Optimization. In Fifteenth International Conference on Machine Learning, Morgan Kaufmann, 1998.
28. S. S. Keerthi, et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation, 13(3):637-649, 2001.