Skip to main content
SpringerLink
Log in

In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project

  • Conference paper
  • First Online:
Discovery Science (DS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2534))

Included in the following conference series:

Abstract

The paper gives an overview of an inter-disciplinary research project whose goal is to elucidate the complex phenomenon of expressive music performance with the help of machine learning and automated discovery methods. The general research questions that guide the project are laid out, and some of the most important results achieved so far are briefly summarized (with an emphasis on the most recent and still very speculative work). A broad view of the discovery process is given, from data acquisition issues through data visualization to inductive model building and pattern discovery. It is shown that it is indeed possible for a machine to make novel and interesting discoveries even in a domain like music. The report closes witha few general lessons learned and withth e identification of a number of open and challenging research problems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. Agrawal, R., and Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago, Chile.

    Google Scholar 

  2. Chen, S.F. (1995). Bayesian Grammar Induction for Language Modeling. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp.228–235.

    Google Scholar 

  3. Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler and F. Roli (Ed.), First International Workshop on Multiple Classifier Systems. New York: Springer Verlag.

    Google Scholar 

  4. Dixon, S. (2001). Automatic Extraction of Tempo and Beat from Expressive Performances. Journal of New Music Research 30(1), 39–58.

    Article  Google Scholar 

  5. Dixon, S. and Cambouropoulos, E. (2000). Beat Tracking with Musical Knowledge. In Proceedings of the 14th European Conference on Artificial Intelligence (ECAI-2000), IOS Press, Amsterdam.

    Google Scholar 

  6. Dixon, S. (2001). An Empirical Comparison of Tempo Trackers. In Proceedings of the VIII Brazilian Symposium on Computer Music (SBCM’01), Fortaleza, Brazil.

    Google Scholar 

  7. Dixon, S., Goebl, W., and Widmer, G. (2002). The Performance Worm: Real Time Visualisation of Expression Based on Langner’s Tempo-Loudness Animation. In Proceedings of the International Computer Music Conference (ICMC’2002), Göteborg, Sweden.

    Google Scholar 

  8. Friberg, A. (1995). A Quantitative Rule System for Musical Performance. Doctoral Dissertation, Royal Institute of Technology (KTH), Stockholm, Sweden.

    Google Scholar 

  9. Gabrielsson, A. (1999). The Performance of Music. In D. Deutsch (Ed.), The Psychology of Music (2nd ed., pp. 501–602). San Diego: Academic Press.

    Google Scholar 

  10. Hunter, L. (ed.) (1993). Artificial Intelligence and Molecular Biology. Menlo Park, CA: AAAI Press.

    Google Scholar 

  11. King, R.D., Muggleton, S., Lewis, R.A., and Sternberg, M.J.E. (1992). Drug Design by Machine Learning: The Use of Inductive Logic Programming to Model the Structure-activity Relationship of Trimethoprim Analogues Binding to Dihydrofolate Reductase. In Proceedings of the National Academy of Sciences, Vol. 89, pp.11322–11326.

    Article  Google Scholar 

  12. Kohonen, T. (2001). Self-Organizing Maps, 3rd edition. Berlin: Springer Verlag.

    Google Scholar 

  13. Langner, J. and Goebl, W. (2002). Representing Expressive Performance in Tempo-Loudness Space. In Proceedings of the ESCOM Conference on Musical Creativity, Liège, Belgium.

    Google Scholar 

  14. Langner, J., and Goebl, W. (2002). Visualizing Expressive Performance in Tempo-Loudness Space. Submitted to Computer Music Journal. Preliminary version available on request from the author.

    Google Scholar 

  15. Ludl, M. and Widmer, G. (2002). Towards a Simple Clustering Criterion Based on Minimum LengthEnco ding. In Proceedings of the 13th European Conference on Machine Learning (ECML’02), Helsinki. Berlin: Springer Verlag.

    Google Scholar 

  16. Mannila, H., Toivonen, H., and Verkamo, I. (1997). Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery, 1(3), 259–289.

    Article  Google Scholar 

  17. Muggleton, S., King, R.D., and Sternberg, M.J.E. (1992). Protein Secondary Structure Prediction Using Logic-based Machine Learning. Protein Engineering 5(7), 647–657.

    Article  Google Scholar 

  18. Nevill-Manning, C.G. and Witten, I.H. (1997). Identifying Hierarchical Structure in Sequences: A linear-time algorithm. Journal of Artificial Intelligence Research 7, 67–82.

    MATH  Google Scholar 

  19. Oliver, J., Baxter, R., and Wallace, C. (1996). Unsupervised Learning Using MML. In Proceedings of the 13th International Conference on Machine Learning (ICML’96). San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  20. Palmer, C. (1988). Timing in Skilled Piano Performance. Ph.D. Dissertation, Cornell University.

    Google Scholar 

  21. Pampalk, E., Rauber, A., and Merkl, D. (2002). Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps. In Proceedings of the International Conference on Artificial Neural Networks (ICANN’2002), Madrid.

    Google Scholar 

  22. Repp, B. (1992). Diversity and Commonality in Music Performance: An Analysis of Timing Microstructure in Schumann’s ‘Träumerei’. Journal of the Acoustical Society of America 92(5), 2546–2568.

    Article  Google Scholar 

  23. Repp, B. H. (1998). A Microcosm of Musical Expression: I. Quantitative Analysis of Pianists’ Timing in the Initial Measures of Chopin’s Etude in E major. Journal of the Acoustical Society of America 104, 1085–1100.

    Article  Google Scholar 

  24. Repp, B. H. (1999). A Microcosm of Musical Expression: II. Quantitative Analysis of Pianists’ Dynamics in the Initial Measures of Chopin’s Etude in E major. Journal of the Acoustical Society of America 105, 1972–1988.

    Article  Google Scholar 

  25. Shavlik, J.W., Towell, G., and Noordewier, M. (1992). Using Neural Networks to Refine Biological Knowledge. International Journal of Genome Research 1(1), 81–107.

    Google Scholar 

  26. Sundberg, J. (1993). How Can Music Be Expressive? Speech Communication 13, 239–253.

    Article  Google Scholar 

  27. Todd, N. (1989). Towards a Cognitive Theory of Expression: The Performance and Perception of Rubato. Contemporary Music Review, vol. 4, pp. 405–416.

    Article  Google Scholar 

  28. Todd, N. (1992). The Dynamics of Dynamics: A Model of Musical Expression. Journal of the Acoustical Society of America 91, pp.3540–3550.

    Article  Google Scholar 

  29. Valdés-Pérez, R.E. (1995). Machine Discovery in Chemistry: New Results. Artificial Intelligence 74(1), 191–201.

    Article  Google Scholar 

  30. Valdés-Pérez, R.E. (1996). A New Theorem in Particle Physics Enabled by Machine Discovery. Artificial Intelligence 82(1-2), 331–339.

    Article  Google Scholar 

  31. Valdés-Pérez, R.E. (1999). Principles of Human-Computer Collaboration for Knowledge Discovery in Science. Artificial Intelligence 107(2), 335–346.

    Article  MATH  Google Scholar 

  32. Widmer, G. (2001). Using AI and Machine Learning to Study Expressive Music Performance: Project Survey and First Report. AI Communications 14(3), 149–162.

    MATH  Google Scholar 

  33. Widmer, G. (2001). The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. In Proceedings of the 12th European Conference on Machine Learning (ECML’01), Freiburg, Germany. Berlin: Springer Verlag.

    Google Scholar 

  34. Widmer, G. (2001). Discovering Strong Principles of Expressive Music Performance withth e PLCG Rule Learning Strategy. In Proceedings of the 12th European Conference on Machine Learning (ECML’01), Freiburg, Germany. Berlin: Springer Verlag.

    Google Scholar 

  35. Widmer, G. (2002). Machine Discoveries: A Few Simple, Robust Local Expression Principles. Journal of New Music Research 31(1) (in press).

    Google Scholar 

  36. Widmer, G., and Tobudic, A. (2002). Playing Mozart by Analogy: Learning Multilevel Timing and Dynamics Strategies. Journal of New Music Research (to appear).

    Google Scholar 

  37. Windsor, L. and Clarke, E. (1997). Expressive Timing and Dynamics in Real and Artificial Musical Performances: Using an Algorithm as an Analytical Tool. Music Perception 15, 127–152.

    Google Scholar 

  38. Zwicker, E. and Fastl, H. (2001). Psychoacoustics. Facts and Models. Springer Series in Information Sciences, Vol.22. Berlin: Springer Verlag.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Cybernetics and Artificial Intelligence, University of Vienna, Austria

    Gerhard Widmer

  2. Austrian Research Institute for Artificial Intelligence, Vienna

    Gerhard Widmer

Authors
  1. Gerhard Widmer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Deutsches Forschungszentrum für Künstliche Intelligenz, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Steffen Lange

  2. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Ken Satoh

  3. Department of Computer Science, University of Maryland, College Park, 20742, Maryland, MD, USA

    Carl H. Smith

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Widmer, G. (2002). In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_4

Download citation

  • DOI: https://doi.org/10.1007/3-540-36182-0_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00188-1

  • Online ISBN: 978-3-540-36182-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation