[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

KDD-Based Approach to Musical Instrument Sound Recognition

  • Conference paper
  • First Online:
Foundations of Intelligent Systems (ISMIS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2366))

Included in the following conference series:

Abstract

Automatic content extraction from multimedia files is a hot topic nowadays. Moving Picture Experts Group develops MPEG-7 standard, which aims to define a unified interface for multimedia content description, including audio data. Audio description in MPEG-7 comprises features that can be useful for any content-based search of sound files. In this paper, we investigate how to optimize sound representation in terms of musical instrument recognition purposes. We propose to trace trends in evolution of values of MPEG-7 descriptors in time, as well as their combinations. Described process is a typical example of KDD application, consisting of data preparation, feature extraction and decision model construction. Discussion of efficiency of applied classifiers illustrates capabilities of further progress in optimization of sound representation. We believe that further research in this area would provide background for automatic multimedia content description.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, I.: Fast Discovery of Association Rules. In: Proc. of the Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT Press, CA (1996) pp. 307–328.

    Google Scholar 

  2. Ando, S., Yamaguchi, K.: Statistical Study of Spectral Parameters in Musical Instrument Tones. J. Acoust. Soc. of America, 94,1, (1993) pp. 37–45.

    Article  Google Scholar 

  3. Bazan, J.G., Nguyen, H.S., Nguyen, S.H, Synak, P., Wróblewski, J.: Rough Set Algorithms in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds), Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Physica-Verlag (2000) pp. 49–88.

    Google Scholar 

  4. Bazan, J.G., Szczuka, M.: RSES and RSESlib-A collection of tools for rough set computations. In: Ziarko, W., Yao, Y.Y. (eds), Proc. of RSCTC’00, Banff, Canada (2000). See also: http://alfa.mimuw.edu.pl/~rses/.

  5. Düntsch I., Gediga G., Nguyen H.S.: Rough set data analysis in the KDD process. In: Proc. of IPMU 2000, Madrid, Spain (2000) vol. 1, pp. 220–226.

    Google Scholar 

  6. Herrera, P., Amatriain, X., Batlle, E., Serra X.: Towards instrument segmentation for music content description: a critical review of instrument classification techniques. In: Proc. of ISMIR 2000, Plymouth, MA (2000).

    Google Scholar 

  7. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Report C-1997-15, University of Helsinki, Finland (1997).

    Google Scholar 

  8. ISO/IEC JTC1/SC29/WG11: Overview of the MPEG-7 Standard. Doc. N4031.

    Google Scholar 

  9. Liu, H., Motoda, H. (eds): Feature extraction, construction and selection-a data mining perspective. Kluwer Academic Publishers, Dordrecht (1998).

    MATH  Google Scholar 

  10. Martin, K.D., Kim, Y.E.: 2pMU9. Musical instrument identification: A pattern-recognition approach. 136-th meeting of the Acoustical Soc. of America (1998).

    Google Scholar 

  11. Nguyen S.H.: Regularity Analysis And Its Applications In Data Mining. Ph.D. Dissertation, Warsaw University, Poland (2000).

    Google Scholar 

  12. Opolko, F., Wapnick, J.: MUMS-McGill University Master Samples. CD’s (1987).

    Google Scholar 

  13. Pawlak, Z.: Rough sets-Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991).

    MATH  Google Scholar 

  14. Pollard, H.F., Jansson, E.V.: A Tristimulus Method for the Specification of Musical Timbre. Acustica, Vol. 51 (1982) pp. 162–171.

    Google Scholar 

  15. Ślȩzak, D., Wróblewski, J.: Classification algorithms based on linear combinations of features. In: Proc. of PKDD’99. Praga, Czech Republik, LNAI 1704, Springer, Heidelberg (1999) pp. 548–553.

    Google Scholar 

  16. Synak, P.: Temporal templates and analysis of time related data. In: Ziarko, W., Yao, Y.Y. (eds), Proc. of RSCTC’00, Banff, Canada (2000).

    Google Scholar 

  17. Wieczorkowska, A.A.: The recognition efficiency of musical instrument sounds de-pending on parameterization and type of a classifier (in Polish), Ph.D. Dissertation, Technical University of Gdańsk, Poland (1999).

    Google Scholar 

  18. Wieczorkowska, A.A., Raś, Z.W.: Audio Content Description in Sound Databases. In: Zhong, N., Yao, Y., Liu, J., Ohsuga, S. (eds), Proc. of WI’01, Maebashi City, Japan, LNCS/LNAI 2198, Springer-Verlag (2001) pp. 175–183.

    Google Scholar 

  19. Wróblewski, J.: Analyzing relational databases using rough set based methods. In: Proc. of IPMU’00. Madrid, Spain (2000) 1, pp. 256–262.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ślȩzak, D., Synak, P., Wieczorkowska, A., Wróblewski, J. (2002). KDD-Based Approach to Musical Instrument Sound Recognition. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_5

Download citation

  • DOI: https://doi.org/10.1007/3-540-48050-1_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43785-7

  • Online ISBN: 978-3-540-48050-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics