Skip to main content

Developing Fitness Functions for Pleasant Music: Zipf’s Law and Interactive Evolution Systems

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3449)

Abstract

In domains such as music and visual art, where the quality of an individual often depends on subjective or hard to express concepts, the automating fitness assignment becomes a difficult problem. This paper discusses the application of Zipf’s Law in evaluation of music pleasantness. Preliminary results indicate that a set of Zipf-based metrics can be effectively used to classify music according to pleasantness as reported by human subjects. These studies suggest that metrics based on Zipf’s law may capture essential aspects of proportion in music as it relates to music aesthetics. We discuss the significance of these results for the automation of fitness assignment in evolutionary music systems.

Keywords

  • Average Success Rate
  • Style Identification
  • Interactive Evolution
  • Fitness Assignment
  • Music Excerpt

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-32003-6_50
  • Chapter length: 10 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   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-32003-6
  • 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   169.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Burton, A.R., Vladimirova, T.: Applications of Genetic Techniques to Musical Composition. Computer Music Journal 23(4), 59–73 (1999)

    CrossRef  Google Scholar 

  2. Spector, L., Alpern, A.: Induction an Recapitulation of Deep Musical Structure. In: IJCAI 1995 Workshop on Artificial Intelligence and Music, pp. 41–48 (1995)

    Google Scholar 

  3. Biles, J.A., Anderson, P.G., Loggi, L.W.: Neural Network Fitness Function for a Musical GA. In: International ICSC Symposium on Intelligent Industrial Automation (IIA 1996) and Soft Computing (SOCO 1996), pp. B39-B44 (1996)

    Google Scholar 

  4. Burton, A.R., Vladimirova, T.: Genetic Algorithm Utilising Neural Network Fitness Evaluation for Musical Composition. In: 1997 International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 220–224 (1997)

    Google Scholar 

  5. Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley Press, New York (1949)

    Google Scholar 

  6. Voss, R.F., Clarke, J.: 1/f Noise in Music and Speech. Nature 258, 317–318 (1975)

    CrossRef  Google Scholar 

  7. Manaris, B., Vaughan, D., Wagner, C., Romero, J., Davis, R.B.: Evolutionary Music and the Zipf–Mandelbrot Law – Progress towards Developing Fitness Functions for Pleasant Music. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 522–534. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  8. Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., Davis, R.B.: Zipf’s Law, Music Classification and Aesthetics. Computer Music Journal 29(1) (2005)

    Google Scholar 

  9. Classical Music Archives (2004), http://www.classicalarchives.com

  10. Stuttgart Neural Network Simulator (2004), http://www-ra.informatik.uni-tuebingen.de/SNNS/

  11. Machado, P., Romero, J., Manaris, B., Santos, A., Cardoso, A.: Power to the Critics - A Framework for the Development of Artificial Critics. In: Proceedings of 3rd Workshop on Creative Systems, 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico, pp. 55–64 (2003)

    Google Scholar 

  12. Machado, P., Romero, J., Santos, M.L., Cardoso, A., Manaris, B.: Adaptive Critics for Evolutionary Artists. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 437–446. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  13. Barrett, L.F., Russell, J.A., The Structure, J.A.: of Current Affect: Controversies and Emerging Consensus. Current Directions in Psychological Science 8(1), 10–14 (1999)

    CrossRef  Google Scholar 

  14. Schubert, E.: Continuous Measurement of Self-report Emotional Response to Music. In: Juslin, P.N., Sloboda, J.A. (eds.) Music and Emotion – Theory and Research, pp. 393–414. Oxford University Press, Oxford (2001)

    Google Scholar 

  15. Russell, S., Norvig, P.: Artificial Intelligence – A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)

    Google Scholar 

  16. Machado, P., Cardoso, A.: All the Truth about NEvAr. Applied Intelligence 16(2), 101–119 (2002)

    MATH  CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Manaris, B., Machado, P., McCauley, C., Romero, J., Krehbiel, D. (2005). Developing Fitness Functions for Pleasant Music: Zipf’s Law and Interactive Evolution Systems. In: , et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-32003-6_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25396-9

  • Online ISBN: 978-3-540-32003-6

  • eBook Packages: Computer ScienceComputer Science (R0)