Skip to main content

Genetic Programming for Musical Sound Analysis

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7247)

Abstract

This study uses Genetic Programming (GP) in developing a classifier to distinguish between five musical instruments. Using only simple arithmetic and boolean operators with 95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94%. The experiment is then run again using only 14 of the most often chosen features. Limiting the features in this way raised the best classification to 94.3% and the average accuracy from 68.2% to 75.67%. This demonstrates that not only can GP be used to create a classifier but it can be used to determine the best features to choose for accurate musical instrument classification, giving an insight into timbre.

Keywords

  • Musical Information Retrieval
  • timbre
  • Genetic Programming

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-29142-5_16
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-29142-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ANSI: American National Standard-Psychoacoustical Terminology. American National Standards Institute, New York (1973)

    Google Scholar 

  2. Brown, J.: Computer identification of musical instruments using pattern recognition with cepstra coefficients as features. Journal of Acoustical Society of America 105(3) (1999)

    Google Scholar 

  3. Eronen, A.: Comparison of features for musical instrument recognition. In: Workshop on Application of Signal Processing to Audio and Acoustics, New York, pp. 19–22 (2001)

    Google Scholar 

  4. Eronen, A., Klapuri, A.: Musical instrument recognition using cepstral coefficients and temporal features. In: ICASSP (2000)

    Google Scholar 

  5. Essid, S., Richard, G., David, B.: Musical instrument recognition by pairwise classification strategies. IEEE Transaction on Audio, Speech and Language Processing 14, 1401–1412 (2006)

    CrossRef  Google Scholar 

  6. Fraser, A., Fujinaga, I.: Toward real-time recognition of acoustic musical instruments. In: ICMC, China, pp. 175–177 (1999)

    Google Scholar 

  7. Goto, M.: Development of the RWC music database. In: Proceedings of the 18th Congress on Acoustics (2004)

    Google Scholar 

  8. Grey, J.: Multidimensional perceptual scaling of musical timbres. Journal of Acoustical Society of America 61, 1270–1277 (1977)

    CrossRef  Google Scholar 

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

    Google Scholar 

  10. Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24, 417–441, 498–520 (1933)

    Google Scholar 

  11. Kaminskyj, I., Materka, A.: Automatic source identification of monophonc musical instrument sounds. In: IEEE International Conference on Neural Networks, pp. 189–194 (1995)

    Google Scholar 

  12. Koza, J.: Genetic evolution and co-evolution of game strategies. In: The International Conference on Game Theory and Its Applications. Stony Brook, New York (1992)

    Google Scholar 

  13. Loughran, R.: Musical Instrument Identification with Feature Selection Using Evolutionary Methods. Ph.D. thesis, University of Limerick (2009)

    Google Scholar 

  14. Loughran, R., Walker, J., O’Neill, M.: An exploration of genetic algorithms for efficient musical instrument identification. In: Irish Signals and Sytems Conference, Dublin, Ireland (2009)

    Google Scholar 

  15. Loughran, R., Walker, J., O’Neill, M., O’Farrell, M.: Comparison of features for musical instrument identification using artificial neural networks. In: CMMR, Copenhagen, Denmark, pp. 19–33 (2008)

    Google Scholar 

  16. MATLAB7: Version 7.2 (r2006a) matlab software. In: The Mathworks (2006)

    Google Scholar 

  17. Opolko, F., Wapnick, J.: McGill university master samples (MUMS) (cds) (1987)

    Google Scholar 

  18. Silva, S.: GPLAB - a genetic programming toolbox for MATLAB (2004), http://gplab.sourceforge.net/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Loughran, R., Walker, J., O’Neill, M., McDermott, J. (2012). Genetic Programming for Musical Sound Analysis. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29142-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29141-8

  • Online ISBN: 978-3-642-29142-5

  • eBook Packages: Computer ScienceComputer Science (R0)