On the usefulness of differentiated transient/steady-state processing in machine recognition of musical instruments

Audio Engineering Society - 118th Convention Spring Preprints 2005

Volumn 2, Issue , 2005, Pages 801-810

  Essid, Slim ^a   Leveau, Pierre ^a,b   Richard, Gäel ^a   Daudet, Laurent ^b   David, Bertrand ^a  

^a TELECOM PARISTECH   (France)

^b Laboratoire D'Acoustique Musicale   (France)

Author keywords

[No Author keywords available]

Indexed keywords

AUDIO SIGNAL; AUTOMATIC SEGMENTATION TECHNIQUES; FEATURE SELECTION ALGORITHM; MACHINE RECOGNITION; MUSICAL SOUNDS; OBJECTIVE CRITERIA; SUPPORT VECTOR MACHINE CLASSIFICATION;

SIGNAL PROCESSING;

ACOUSTICS;

EID: 84866507360     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. M. Clark, P. Robertson, and D. A. Luce. A preliminary experiment on the perceptual basis for musical instrument families. Journal of the Audio Engenieering Society, 12:199-203, 1964.
2. McAdams S., Winsberg S., de Soete G., and Krimphoff J. Perceptual scaling of synthesized musical timbres: common dimensions, specficities and latent subject classes. Psychological Research, (58):177-192, 1995.
3. A. Eronen. Comparison of features for musical instrument recognition. In Proceedings of WASPAA, 2001.
4. J.P. Bello, C. Duxbury, M. Davies, and M.B. Sandler. On the use of phase and energy for musical onset detection in the complex domain. IEEE Signal Processing Letters, 2004.
5. J.P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. Davies, and M.B. Sandler. A tutorial on onset detection in music signals. IEEE Transactions on Speech and Audio Processing, 2005. to be published.
6. P. Leveau, L. Daudet, and G. Richard. Methodology and tools for the evaluation of automatic onset detection algorithms in music, submitted. Proceedings of ISMIR 2004, 2004.
7. M. Goodwin and C. Avendano. Enhancement of audio signals using transient detection and modification. In Proceedings of the 117th AES Convention, 2004.
8. Geoffroy Peeters. A large set of audio features for sound description (similarity and classification) in the cuidado project. Technical report, IRCAM, 2004.
9. Judith C. Brown. Musical instrument identification using autocorrelation coefficients. In International Symposium on Musical Acoustics, pages 291-295, 1998.
10. Antti Eronen. Automatic musical instrument recognition. Master's thesis, Tampere University of Technology, April 2001.
11. Lawrence R. Rabiner. Fundamentals of Speech Processing. Prentice Hall Signal Processing Series. PTR Prentice-Hall, Inc., 1993.
12. Olivier Gillet and Gaël Richard. Automatic transcription of drum loops. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montral, Canada, May 2004.
13. Slim Essid, Gaël Richard, and Bertrand David. Efficient musical instrument recognition on solo performance music using basic features. In AES 25th International Conference, London, UK, June 2004.
14. Information technology - multimedia content description interface - part 4: Audio, jun 2001. ISO/IEC FDIS 15938-4:2001(E).
15. Judith C. Brown, Olivier Houix, and Stephen McAdams. Feature dependence in the automatic identification of musical woodwind instruments. Journal of the Acoustical Society of America, 109:1064-1072, March 2000.
16. Slim Essid, Gaël Richard, and Bertrand David. Musical instrument recognition based on class pairwise feature selection. In 5th International Conference on Music Information Retrieval (ISMIR), Barcelona, Spain, October 2004.
17. Ron Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence Journal, 97(1-2):273-324, 1997. (Pubitemid 127401107)
18. A. L. Blum and P Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence Journal, 97(1-2):245-271, December 1997. (Pubitemid 127401106)
19. I. Guyon and A Elisseeff. An introduction to feature and variable selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
20. Geoffroy Peeters. Automatic classification of large musical instrument databases using hierarchical classifiers with inertia ratio maximization. In 115th AES convention, New York, USA, October 2003.
21. Slim Essid, Gaël Richard, and Bertrand David. Musical instrument recognition by pairwise classification strategies. IEEE Transactions on Speech and Audio Processing, 2004. to be published.
22. Richard Duda and P. E. Hart. Pattern Classification and Scence Analysis. Wiley- Interscience. John Wiley & Sons, 1973.
23. Vladimir Vapnik. The nature of statistical learning theory. Springer-Verlag, 1995.
24. Christopher J.C. Burges. A tutorial on support vector machines for pattern recognition. Journal of Data Mining and knowledge Discovery, 2(2):1-43, 1998.
25. B. Sholkopf and A. J. Smola. Learning with kernels. The MIT Press, Cambridge, MA, 2002.
26. John C. Platt. Probabilistic outputs for support vector machines and comparisions to regularized likelihood methods. 1999. AES 118th Convention, Barcelona, Spain, 2005 May 28-31 Page 10 of 10.