Abstract
The high volume of digital music recordings in the internet repositories has brought a tremendous need for a cooperative recommendation system to help users to find their favorite music pieces. Music instrument identification is one of the important subtasks of a content-based automatic indexing, for which authors developed novel new temporal features and built a multi-hierarchical decision system S with all the low-level MPEG7 descriptors as well as other popular descriptors for describing music sound objects. The decision attributes in S are hierarchical and they include Hornbostel-Sachs classification and generalization by articulation. The information richness hidden in these descriptors has strong implication on the confidence of classifiers built from S. Rule-based classifiers give us approximate definitions of values of decision attributes and they are used as a tool by content-based Automatic Indexing Systems (AIS). Hierarchical decision attributes allow us to have the indexing done on different granularity levels of classes of music instruments. We can identify not only the instruments playing in a given music piece but also classes of instruments if the instrument level identification fails. The quality of AIS can be verified using precision and recall based on two interpretations: user and system-based [16]. AIS engine follows system-based interpretation.
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
Balzano, G.J.: What are musical pitch and timbre? Music Perception, an interdisciplinary Journal 3, 297–314 (1986)
Bay, M., Beauchamp, J.W.: Harmonic source separation using prestored spectra. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 561–568. Springer, Heidelberg (2006)
Bregman, A.S.: Auditory scene analysis, the perceptual organization of sound. MIT Press, Cambridge (1990)
Cadoz, C.: Timbre et causalite, unpublished paper, Seminar on Timbre, Institute de Recherche et Coordination Acoustique/Musique, Paris, France (April 13-17, 1985)
Cessie, S., Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1), 191–201 (1992)
Fujinaga, I., McMillan, K.: Real time recognition of orchestral instruments. In: Proceedings of the International Computer Music Conference, pp. 141–143 (2000)
Gaasterland, T.: Cooperative answering through controlled query relaxation. IEEE Expert 12(5), 48–59 (1997)
Kinoshita, T., Sakai, S., Tanaka, H.: Musical sound source identification based on frequency component adaptation. In: Proceedings of IJCAI Workshop on Computational Auditory Scene Analysis (IJCAI-CASA 1999), Stockholm, Sweden, pp. 18–24 (July-August 1999)
Kitahara, T., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Instrument identification in polyphonic music: feature weighting to minimize influence of sound overlaps. EURASIP Journal on Advances in Signal Processing 1, 155–155 (2007)
Lewis, R., Zhang, X., Ras, Z.W.: Knowledge discovery based identification of musical pitches and instruments in polyphonic sounds. Journal of Engineering Applications of Artificial Intelligence 20(5), 637–645 (2007)
Lindsay, A.T., Herre, J.: MPEG-7 and MPEG-7 Audio-An Overview. J. Audio Eng. Soc. 49, 589–594 (2001)
Ozerov, A., Philippe, P., Gribonval, R., Bimbot, F.: One microphone singing voice separation using source adapted models. In: Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 90–93 (2005)
Pawlak, Z.: Information systems - theoretical foundations. Information Systems Journal 6, 205–218 (1991)
Pollard, H.F., Jansson, E.V.: A tristimulus Method for the specification of Musical Timbre. Acustica 51, 162–171 (1982)
Ras, Z.W., Dardzińska, A.: Solving Failing Queries through Cooperation and Collaboration. World Wide Web Journal, Special Issue on Web Resources Access 9(2), 173–186 (2006)
Ras, Z.W., Dardzińska, A., Zhang, X.: Cooperative Answering of Queries based on Hierarchical Decision Attributes. CAMES Journal, Polish Academy of Sciences, Institute of Fundamental Technological Research 14(4), 729–736 (2007)
Ras, Z.W., Zhang, X., Lewis, R.: MIRAI: Multi-hierarchical, FS-tree based Music Information Retrieval System. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 80–89. Springer, Heidelberg (2007)
Scheirer, E., Slaney, M.: Construction and Evaluation of a Robust Multi-feature Speech/Music Discriminator. In: Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (1997)
Smith, J.O., Serra, X.: PARSHL: An Analysis/Synthesis Program for Non Harmonic Sounds Based on a Sinusoidal Representation. In: Proc. Int. Computer Music Conf., Urbana-Champaign, Illinois, pp. 290–297 (1987)
Tzanetakis, G., Cook, P.: Musical Genre Classification of Audio Signals. IEEE Trans. Speech and Audio Processing 10, 293–302 (2002)
Vincent, E.: Musical source separation using time-frequency source priors. IEEE Transactions on Audio, Speech and Language Processing 14(1), 91–98 (2006)
Wieczorkowska, A.: Classification of musical instrument sounds using decision trees. In: Proceedings of the 8th International Symposium on Sound Engineering and Mastering, ISSE 1999, pp. 225–230 (1999)
Wieczorkowska, A., Ras, Z., Zhang, X., Lewis, R.: Multi-way Hierarchic Classification of Musical Instrument Sounds. In: Proceedings of the International Conference on Multimedia and Ubiquitous Engineering (MUE 2007), Seoul, South Korea, pp. 897–902. IEEE Computer Society, Los Alamitos (2007)
Wold, E., Blum, T., Keislar, D., Wheaton, J.: Content-Based Classification, Search and Retrieval of Audio. IEEE Multimedia, Fall, 27–36 (1996)
Zhang, X., Marasek, K., Ras, Z.W.: Maximum likelihood study for sound pattern separation and recognition. In: Proceedings of the IEEE CS International Conference on Multimedia and Ubiquitous Engineering (MUE 2007), Seoul, Korea, April 26-28, 2007, pp. 807–812 (2007)
Zhang, X., Ras, Z.W.: Sound isolation by harmonic peak partition for music instrument recognition, in the Special Issue on Knowledge Discovery. Fundamenta Informaticae Journal 78(4), 613–628 (2007)
Zhang, X., Ras, Z.W.: Differentiated Harmonic Feature Analysis on Music Information Retrieval For Instrument Recognition. In: Proceeding of IEEE International Conference on Granular Computing, Atlanta, Georgia, May 10-12, 2006, pp. 578–581 (2006)
Zhang, X., Ras, Z.W.: Analysis of sound features for music timbre recognition. In: Proceedings of the IEEE CS International Conference on Multimedia and Ubiquitous Engineering (MUE 2007), Seoul, Korea, April 26-28, 2007, pp. 3–8 (2007)
ISO/IEC JTC1/SC29/WG11, MPEG-7 Overview (2002), http://mpeg.telecomitalialab.com/standards/mpeg-7/mpeg-7.htm
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, X., Raś, Z.W., Dardzińska, A. (2008). Discriminant Feature Analysis for Music Timbre Recognition and Automatic Indexing. In: Raś, Z.W., Tsumoto, S., Zighed, D. (eds) Mining Complex Data. MCD 2007. Lecture Notes in Computer Science(), vol 4944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68416-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-68416-9_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68415-2
Online ISBN: 978-3-540-68416-9
eBook Packages: Computer ScienceComputer Science (R0)