Abstract
Convenient searching of multimedia databases requires well annotated data. Labeling sound data with information like pitch or timbre must be done through sound analysis. In this paper, we deal with the problem of automatic classification of musical instrument on the basis of its sound. Although there are algorithms for basic sound descriptors extraction, correct identification of instrument still poses a problem. We describe difficulties encountered when classifying woodwinds, brass, and strings of contemporary orchestra. We discuss most difficult cases and explain why these sounds cause problems. The conclusions are drawn and presented in brief summary closing the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Batle, E. and Cano, P. (2000) Automatic Segmentation for Music Classification using Competitive Hidden Markov Models. Proceedings of International Symposium on Music Information Retrieval. Plymouth, MA.
Brown, J. C. (1999) Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. J. Acoust. Soc. of America, 105, 1933–1941
Brown, J. C., Houix, O., and McAdams, S. (2001) Feature dependence in the automatic identification of musical woodwind instruments. J. Acoust. Soc. of America, 109, 1064–1072
Cosi, P., De Poli, G., and Lauzzana, G. (1994) Auditory Modelling and SelfOrganizing Neural Networks for Timbre Classification. Journal of New Music Research, 23, 71–98
Eronen, A. and Klapuri, A. (2000) Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2000. Plymouth, MA. 753–756
Fujinaga, I. and McMillan, K. (2000) Realtime recognition of orchestral instruments. Proceedings of the International Computer Music Conference. 141–143
Herrera, P., Amatriain, X., Batlle, E., and Serra X. (2001) Towards instrument segmentation for music content description: a critical review of instrument classification techniques. In: Proc. of ISMIR 2000, Plymouth, MA
Hornbostel, Erich M. v. and Sachs, C. (1914) Systematik der Musikinstrumente. Ein Versuch. Zeitschrift für Ethnologie, 46, (4–5):553–90. Available at http://www.uni-bamberg.de/ppp/ethnomusikologie/HS-Systematik/HSSystematik
ISO/IEC JTC1/SC29/WG11 (2002) MPEG-7 Overview. Available at http://www.mpeg.telecomitalialab.com/standards/mpeg-7/mpeg-7.htm
Kaminskyj, I. (2000) Multi-feature Musical Instrument Classifier. MikroPolyphonie 6 (online journal at http://farben.latrobe.edu.au/)
Kostek, B. and Czyzewski, A. (2001) Representing Musical Instrument Sounds for Their Automatic Classification. J. Audio Eng. Soc., 49(9), 768–785
Krimphoff, J., Mcadams, S., and Winsberg, S. (1994) Caractérisation du Timbre des Sons Complexes. II. Analyses acoustiques et quantification psychophysique. Journal de Physique IV, Colloque C5, J. de Physique III, 4,3ème Congrès Français d’Acoustique, I, 625–628
Lindsay, A. T. and Herre, J. (2001) MPEG-7 and MPEG-7 Audio — An Overview. J. Audio Eng. Soc., 49(7/8), 589–594
Martin, K. D. and Kim, Y. E. (1998) 2pMU9. Musical instrument identification: A pattern-recognition approach. 136-th meeting of the Acoustical Soc. of America, Norfolk, VA
Opolko, F. and Wapnick, J. (1987) MUMS — McGill University Master Samples. CDs
Pollard, H. F. and Jansson, E. V. (1982) A Tristimulus Method for the Specification of Musical Timbre. Acustica, 51, 162–171
SIL International (1999) 534 Musical Instruments subcategories. http://www.sil.org/LinguaLinks/Anthropology/ ExpnddEthnmsclgyCtgrCltrlMtrls/MusicalInstrumentsSubcategorie.htm
Ślçzak, D., Synak, P., Wieczorkowska, A., and Wróblewski, J. (2002) KDDbased approach to musical instrument sound recognition. In Hacid M.-S., Raś Z., Zighed D. A., Kodratoff Y. (Eds.), Foundations of Intelligent Systems. Proc. 13th International Symposium ISMIS 2002. LNAI 2366, Springer, 29–37
Wieczorkowska, A. A.(1999) The recognition efficiency of musical instrument sounds depending on parameterization and type of a classifier (in Polish), Ph.D. Dissertation, Technical University of Gdańsk, Gdańsk.
Wieczorkowska, A. (1999) Rough Sets as a Tool for Audio Signal Classification. In Z. W. Ras, A. Skowron (Eds.), Foundations of Intelligent Systems, LNCS/LNAI 1609, Springer, 367–375
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wieczorkowska, A.A., Wróblewski, J., Ślçzak, D., Synak, P. (2003). Problems with Automatic Classification of Musical Sounds. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36562-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-36562-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00843-9
Online ISBN: 978-3-540-36562-4
eBook Packages: Springer Book Archive