Music genre classification based on ensemble of signals produced by source separation methods

Intelligent Decision Technologies

Volumn 4, Issue 3, 2010, Pages 229-237

  Lampropoulos, Aristomenis S ^a   Lampropoulou, Paraskevi S ^a   Tsihrintzis, George A ^a  

^a UNIVERSITY OF PIRAEUS   (Greece)

Author keywords

Ensemble; Music genre classification; Source separation; Support vector machine

Indexed keywords

EID: 85013581893     PISSN: 18724981     EISSN: 18758843     Source Type: Journal    
DOI: 10.3233/IDT-2010-0083     Document Type: Article

References (28)

1. W.J. Dowling and D.L. Harwood, Music Congition, Academic Press, 1996.
2. J.J. Aucoutier and F. Pachet, Representing musical genre: A state of the art, Journal of New Music Research 32(1) (2003), 83-93.
3. G. Tzanetakis and P. Cook, Musical genre classification of audio signals, IEEE Transactions on Speech and Audio Processing 10(5) (July 2002).
4. G. Tzanetakis and P. Cook, Marsyas: A framework for audio analysis, Organised Sound 4(3) (2000).
5. B. Kostek, Musical instrument classification and duet analysis employing music information retrieval techniques.
6. K. Martin, Sound-source recognition: A Theory and Computational Mode, Ph.D. dissertation, MIT, 1999.
7. T. Kitahara, M. Goto and H.G. Okuno, Pitch-dependent musical instrument identification and its application to musical sound ontology, in: IEA/AIE, ser. Lecture Notes in Computer Science, P.W.H. Chung, C.J. Hinde and M. Ali, eds, vol. 2718. Springer, 2003, pp. 112-122.
8. A.S. Lampropoulos, P.S. Lampropoulou and G.A. Tsihrintzis, Musical genre classification of audio data using source separation techniques, presented at the Proc. 5thEURASIP Conference on Speech and Image Processing, Multimedia Communications and Services, Smolenice, The Slovak Republic, July 2005.
9. A.S. Lampropoulos, P.S. Lampropoulou and G.A. Tsihrintzis, Musical genre classification enhanced by source separation techniques, presented at the Proc. 6th International Conference on Music Information Retrieval, London, UK, September 2005, 576-581.
10. P.S. Lampropoulou, A.S. Lampropoulos and G.A. Tsihrintzis, Musical instrument category discrimination using waveletbased source separation, presented at the Proc. KESIIMSS2008 1st International Symposium on Intelligent Interactive Multimedia Systems and Services, Piraeus, Greece, July 2008.
11. T. Li, M. Ogihara and Q. Li, A comparative study on content based music genre classification, in Proc. 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, Toronto, Canada, August 2003.
12. M.F. McKinney and J. Breebaart, Features for audio and music classification, presented at the Proc. 4th International Conference on Music Information Retrieval, Washington, D.C., USA, October 2003.
13. M. Mandel and D. Ellis, Song-level features and support vector machines for music classification, presented at the Proc. 6th International Conference on Music Information Retrieval, London, UK, September 2005.
14. T. Lidy and A. Rauber, Evaluation of feature extractors and psycho-acoustic transformations for music genre classification, presented at the Proc. 6th International Conference on Music Information Retrieval, London, UK, September 2005.
15. B. Logan and A. Salomon, A music similarity function based on signal analysis, presented at the Proc, International Conference on Multimedia and Expo, Tokyo, Japan, 2003.
16. E. Pampalk, A. Flexer and G.Widmer, Improvements of audiobased music similarity and genre classification, presented at the Proc. 6th International Conference on Music Information Retrieval, London, UK, September 2005.
17. D. Turnbull and C. Elkan, Fast recognition of musical genres using rbf networks, IEEE Transactions on Knowledge and Data Engineering 17(4), 2005.
18. T.Virtanen, Separation of sound sources by convolutive sparse coding, presented at the Proc. of Workshop on Statistical and Perceptual Audio Processing (SAPA), 2004.
19. M.A. Casey and A. Westner, Separation of mixed audio sources by independent subspace analysis, presented at the International Computer Music Conference (ICMC), 2000.
20. P. Smaragdis and J. Brown, Non-negative matrix factorization for polyphonic music transcription, in: In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2003.
21. K. Zhang and L.-W. Chan, An adaptive method for subband decomposition ica, Neural Comput 18(1) (2006), 191-223.
22. I. Koprivaa and D. Sersic, Wavelet packets approach to blind separation of statistically dependent sources, Neurocomputing doi: 10.1016/j.neucom.2007.04.002, 2007.
23. M.V. Wickerhauser, Adapted wavelet analysis from theory to software. Natick, MA, USA: A.K. Peters, Ltd., 1994.
24. S. Mallat, A Wavelet Tour of Signal Processing, Second Edition (Wavelet Analysis and Its Applications). Academic Press, September 1999.
25. J.L. Marchini, C. Heaton et al., The fastica package - fastica algorithms to perform ica and projection pursuit.
26. T.-K. Huang, R.C. Weng and C.-J. Lin, Generalized bradleyterry models and multi-class probability estimates, J Mach Learn Res 7 (2006), 85-115.
27. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001, software available at http: //www. csie.ntu.edu.tw/cjlin/libsvm.
28. C.Wei Hsu and C.-J. Lin, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks 13 (2006), 415-425.