Temporal integration for audio classification with application to musical instrument classification

IEEE Transactions on Audio, Speech and Language Processing

Volumn 17, Issue 1, 2009, Pages 174-186

  Joder, Cyril ^a   Essid, Slim ^a   Richard, Gaël ^a  

^a INSTITUT TELECOM   (France)

Author keywords

Alignment kernels; Audio classification; Music information retrieval (MIR); Musical instrument recognition; Support vector machine (SVM); Temporal feature integration

Indexed keywords

AUDIO CLASSIFICATION; CLASSIFICATION SYSTEM; EXPERIMENTAL STUDIES; INSTRUMENT RECOGNITION; MUSIC INFORMATION RETRIEVAL; MUSIC INFORMATION RETRIEVAL (MIR); MUSIC SIMILARITY; MUSICAL AUDIO; MUSICAL GENRE; MUSICAL PHRASE; STATE OF THE ART; SUPPORT VECTOR MACHINE (SVM); TEMPORAL FEATURE INTEGRATION; TEMPORAL INTEGRATION; TEMPORAL PROPERTY; TIME HORIZONS; VARIABLE FRAME LENGTH;

ALIGNMENT; AUDIO ACOUSTICS; AUTOMATIC INDEXING; CLASSIFICATION (OF INFORMATION); IMAGE RETRIEVAL; INFORMATION SERVICES; INSTRUMENTS; INTEGRATION; MUSICAL INSTRUMENTS; SUPPORT VECTOR MACHINES;

ELECTRONIC MUSICAL INSTRUMENTS;

EID: 70350482320     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.2007613     Document Type: Article

References (53)

1. M. Ryynänen and A. Klapuri, "Automatic bass line transcription from streaming polyphonic audio," in Proc. IEEE ICASSP, Honolulu, HI, Apr. 2007, pp. IV-1437-IV-1440.
2. J. Marques and P. Moreno, "A study of musical instrument classification using Gaussian mixture models and support vector machines," Tech. Rep. Compaq Computer Corp., Cambridge, MA, 1999.
3. A. Eronen, "Comparison of features for musical instrument recognition," in Proc. IEEEWorkshop Applicat. Signal Process. Audio Acoust., 2001, pp. 19-22.
4. M. A. Loureiro, H. B. de Paula, and H. C. Yehia, "Timbre classification of a single musical instrument," in Proc. Int. Symp. Music Inf. Retrieval, 2004, pp. 546-549.
5. A. Verma, T. Faruquie, C. Neti, S. Basu, and A. Senior, "Late integration in audio-visual continuous speech recognition," in Proc. Automatic Speech Recognition and Understanding Workshop (ASRU), 1999.
6. A. Meng, "Temporal feature integration for music organisation," Ph.D. dissertation, Technical Univ. of Denmark, Lyngby, Denmark, 2006.
7. S. Dubnov and X. Rodet, "Timbre recognition with combined stationary and temporal features," in Proc. Int. Comput. Music Conf., 1998.
8. L. G. Martins, J. J. Burred, G. Tzanetakis, and M. Lagrange, "Polyphonic instrument recognition using spectral clustering," in Proc. ISMIR, 2007, pp. 213-218.
9. M. Eichner, M. Wolff, and R. Hoffmann, "Instrument classification using hidden Markov models," in Proc. ISMIR, 2006, pp. 349-350.
10. G. Tzanetakis, G. Essl, and P. Cook, "Automatic musical genre classification of audio signals," in Proc. ISMIR, 2001.
11. M. Mandel and D. Ellis, "Song-level features and SVMs for music classification," in Proc. ISMIR, 2005, pp. 594-599.
12. N. Scaringella and G. Zoia, "On the modeling of time information for automatic genre recognition systems in audio signals," in Proc. ISMIR, 2005, pp. 666-671.
13. M. Slaney, "Semantic-audio retrieval," in IEEE Int. Conf. Acoust., Speech, Signal Process., 2002, vol.4, pp. IV-1408-IV-1411.
14. M. F. McKinney and J. Breebart, "Features for audio and music classification," in Proc. Int. Symp. Music Inf. Retrieval, 2003, pp. 151-158.
15. M. Athineos, H. Hermansky, and D. Ellis, "Lp-trap: Linear predictive temporal patterns," in Proc. Int. Conf. Spoken Lang. Process., 2004, pp. 1154-1157.
16. A. Meng, P. Ahrendt, J. Larsen, and L. K. Hansen, "Temporal feature integration for music genre classification," IEEE Trans. Audio, Speech, Lang. Process., pp. 1654-1664, Jul. 2006.
17. A. Eronen, "Musical instrument recognition using ica-based transform of features and discriminatively trained hmms," in Proc. 7th Int. Symp. Signal Process. and its Applicat., 2003, pp. 133-136.
18. T. Kitahara, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, "Musical instrument recognizer "instrogram" and its application to music retrieval based on instrumentation similarity," in Proc. IEEE Int. Symp. Multimedia, 2006, pp. 265-274.
19. C. Bahlmann, B. Haasdonk, and H. Burkhardt, "On-line handwriting recognition with support vector machines-A kernel approach," in Proc. 8th IWFHR, 2002, pp. 49-54.
20. H. Shimodaira, K. Noma, M. Nakai, and S. Sagayama, "Dynamic timealignment kernel in support vector machine," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2002.
21. M. Cuturi, J.-P. Vert, O. Birkenes, and T. Matsui, "A kernel for time series based on global alignments," in Proc. IEEE ICASSP, Apr. 2007, vol.2, pp. 413-416.
22. J. Grey, "Multidimensional perceptual scaling of musical timbres," J. Acoust. Soc. Amer., pp. 1270-1277, 1977.
23. S. Essid, G. Richard, and B. David, "Instrument recognition in polyphonic music based on automatic taxonomies," IEEE Trans. Audio, Speech, Lang. Process., vol.14, no.1, pp. 68-80, Jan. 2006.
24. I. Kaminskyj and A. Materka, "Automatic source identification of monophonic musical instrument sounds," in Proc. IEEE Int. Conf. Neural Netw., 1995, pp. 189-194.
25. K. D. Martin, "Sound-source recognition: A theory and computational model," Ph.D. dissertation, Mass. Inst. Technol., Cambridge, 1999.
26. A. Eronen, "Automatic Musical Instrument Recognition," M.S. thesis, Tampere Univer. of Technol., Tampere, Finland, 2001.
27. T. Kitahara, M. Goto, K. Komatani, T. Ogata, and H. Okuno, "Instrument identification in polyphonic music: Feature weighting with mixed sounds, pitch-dependent timber modeling, and use of musical context," in Proc. ISMIR, 2005, pp. 558-563.
28. S. Essid, "Classification automatique des signaux audio-fréquences : Reconnaissance des instruments de musique," Ph.D. dissertation, École Nationale Supérieure des Télé communications, Paris, France, 2005.
29. P. Leveau, L. Daudet, and G. Richard, "Methodology and tools for the evaluation of automatic onset detection algorithms in music," in Proc. ISMIR, 2004, pp. 72-75.
30. J. Bello, C. Duxburry,M. Davies, and M. Sandler, "On the use of phase and energy for musical onset detection in the complex domain," IEEE Signal Process. Lett., vol.11, no.6, pp. 553-556, Jun. 2004.
31. J. C. Brown, O. Houix, and S. McAdams, "Feature dependence in the automatic identification of musical woodwind instruments," J. Acoust. Soc. Amer., pp. 1064-1072, 2000.
32. S. Essid, G. Richard, and B. David, "Musical instrument recognition based on class pairwise feature selection," in Proc. ISMIR, 2004, pp. 560-567.
33. G. Peeters, "Automatic classification of large musical instrument databases using hierarchical classifiers with inertia ratio maximization," in Proc. 115th AES Convention, 2003.
34. G. Peeters, "A Large Set of Audio Features for Sound Description (Similarity and Classification) in the Cuidado Project," Tech. Rep. IRCAM, 2004.
35. ISO/IEC, "Information Technology - Multimedia Content Description Interface - Part 4: Audio", , Jun. 2001, Int. Standard ISO/IEC FDIS 15938-15944: 2001(E).
36. E. Scheirer and M. Slaney, "Construction and evaluation of a robust multifeature speech/music discriminator," in Proc. ICASSP '97, Munich, Germany, 1997, pp. 1331-1334.
37. T. Li and M. Ogihara, "Music genre classification with taxonomy," in Proc. Int. Conf. Spoken Lang. Process., 2005, pp. 197-200.
38. I. Guyon and A. Elisseeff, "An introduction to feature and variable selection," J. Mach. Learn. Res., pp. 1157-1182, 2003.
39. R. Duda, P. Hart, and D. E. Stork, Pattern Classification. New York: Wiley-Interscience, 2001.
40. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2001.
41. J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA: MIT Press, 1999.
42. P. Delsarte and Y. V. Genin, "The split Levinson algorithm," IEEE Trans. Acoust., Speech, Signal Process., vol.ASSP-34, no.3, pp. 470-478, Jun. 1986.
43. A. Rabaoui, M. Davy, S. Rossignol, Z. Lachiri, and N. Ellouze, "Using one-class SVMs and wavelets for audio surveillance systems," IEEE Trans. Inf. Forensic Security, 2007, submitted for publication.
44. A. Meng and J. Shawe-Taylor, "An investigation of feature models for music genre classification using the support vector classifier," in Proc. ISMIR, 2005, pp. 604-609.
45. J. Kittler, M. Hatef, R. P. Duin, and J. Matas, "On combining classifiers," IEEE Trans. Pattern Anal. Mach. Intell., vol.20, no.3, pp. 226-239, Mar. 1998.
46. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol.77, no.2, pp. 257-286, Feb. 1989.
47. J. A. Bilmes, "What hmms can do," IEICE - Trans. Inf. Syst., vol.E89-D, no.3, pp. 869-891, 2006.
48. J.-P. Vert, H. Saigo, and T. Akutsu, Local Alignment Kernels for Biological Sequences. Cambridge, MA: MIT Press, 2004, pp. 131-154.
49. M. Goto, RWC Music Database: Popular, Classical, and Jazz Music Databases, 2002.
50. M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, RWC Music Database: Music Genre Database and Musical Instrument Sound Database, 2003.
51. T. Schneider and A. Neumaier, Arfit [Online]. Available: http://www.gps.caltech.edu/tapio/arfit/
52. T. Joachims, SVM-Light Toolbox [Online]. Available: http://svmlight. joachims.org/
53. K. Murphy,HMMMatlab Toolbox [Online]. Available: http://www.cs. ubc.ca/murphyk/Software/HMM/hmm.html.