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Rough sets as a tool for audio signal classification

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Foundations of Intelligent Systems (ISMIS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

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Abstract

An application of RS knowledge discovery methods for automatic classification of musical instrument sounds is presented. Also, we provide basic information on acoustics of musical instruments. Since the digital record of sound contains a huge amount of data, the redundancy in the data is fixed via parameterization. The parameters extracted from sounds of musical instrument are discussed. We use quantization as a preprocessing for knowledge discovery to limit the number of parameter values. Next we show exemplary methods of quantization of parameter values. Finally, experiments concerning audio signal classification using rough set approach are presented and the results are discussed.

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References

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

    Article  Google Scholar 

  2. Chmielewski M. R., Grzymala-Busse J. W., Peterson N. W., Than S., The Rule Induction System LERS—a Version for Personal Computers, Foundations of Computing and Decision Sciences, Vol. 18, No. 3-4, 1993, Institute of Computing Science, Technical University of Poznan, Poland, 181–212.

    Google Scholar 

  3. Chmielewski M. R., Grzymala-Busse J. W., Global Discretization of Continuous Attributes as Preprocessing for Machine Learning, 3 rd International Workshop on Rough Sets and Soft Computing, Conference Proceedings, San Jose, CA, 1994, 294–301.

    Google Scholar 

  4. Fletcher N. H., Rossing T. D., The Physics of Musical Instruments, Springer-Verlag, 1991.

    Google Scholar 

  5. Kostek B., Wieczorkowska A., A System for Musical Sound Parameter Database Creation and Analysis, 102nd AES Convention, preprint 4498, Munich 1997.

    Google Scholar 

  6. Kostek B., Wieczorkowska A., Parametric Representation of Musical Sounds, Archives of Acoustics, 22, 1, 1997, 3–26.

    Google Scholar 

  7. Kronland-Martinet R., Grossmann A., Application of Time-Frequency and Time-Scale Methods (Wavelet Transforms) to the Analysis, Synthesis, and Transformation of Natural Sounds, in: De Poli G., Piccialli A., Roads C. (ed.), Representations of Musical Signals, MIT Press, Cambridge, Massachusetts, 1991, 45–85.

    Google Scholar 

  8. Meyer J., The Sound of the Orchestra, J. Audio Eng. Soc., Vol. 41, No. 4, 1993, 203–213.

    Google Scholar 

  9. Øhrn A., Komorowski J., ROSETTA—A Rough Set Toolkit for Analysis of Data, Proc. 3 rd Int. Joint Conf. on Information Sciences, Durham NC, 1997, Vol. 3, 403–407.

    Google Scholar 

  10. Opolko F., Wapnick J., MUMS—McGill University Master Samples, compact discs, McGill University, Montreal, Canada, 1987.

    Google Scholar 

  11. Pawlak Z., Hard and Soft Sets, ICS Research Report 10/94, Warsaw University of Technology, February 1994.

    Google Scholar 

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

    Google Scholar 

  13. Press W. H., Flannery B. P., Teukolsky S. A., Vetterling W. T., Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, 1987.

    Google Scholar 

  14. Reduct Systems, DATALOGIC/R, 1990–95 Reduct Systems Inc., Regina, Saskatchewan, Canada.

    Google Scholar 

  15. Risset J.-C., Timbre Analysis by Synthesis: Representations, Imitations, and Variants for Musical Composition, in: De Poli G., Piccialli A., Roads C. (ed.), Representations of Musical Signals, MIT Press, Cambridge, Massachusetts, 1991, 7–43.

    Google Scholar 

  16. Skowron A., Nguyen S. H., Quantization of Real Value Attributes: Rough Set and Boolean Approach, ICS Research Report 11/95, Warsaw University of Technology.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Technical University of Gdansk, ul. Narutowicza 11/12, 80-952, Gdansk, Poland

    Alicja Wieczorkowska

  2. UNC-Charlotte, Computer Science, 28223, Charlotte, NC, USA

    Alicja Wieczorkowska

Authors
  1. Alicja Wieczorkowska
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Zbigniew W. Raś Andrzej Skowron

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© 1999 Springer-Verlag Berlin Heidelberg

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Wieczorkowska, A. (1999). Rough sets as a tool for audio signal classification. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095123

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  • DOI: https://doi.org/10.1007/BFb0095123

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

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