In search of the Horowitz factor: Interim report on a musical discovery project

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2534, Issue , 2002, Pages 13-32

  Widmer, Gerhard ^a,b  

^a UNIVERSITY OF VIENNA   (Austria)

^b AUSTRIAN RESEARCH INSTITUTE FOR ARTIFICIAL INTELLIGENCE   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION;

AUTOMATED DISCOVERY; EXPRESSIVE MUSIC PERFORMANCE; GENERAL RESEARCH QUESTIONS; INDUCTIVE MODELING; INTER-DISCIPLINARY RESEARCHES; INTERIM REPORTS; PATTERN DISCOVERY; RESEARCH PROBLEMS;

DATA VISUALIZATION;

EID: 84949817459     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36182-0_4     Document Type: Conference Paper

References (38)

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.
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.
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.
4. Dixon, S. (2001). Automatic Extraction of Tempo and Beat from Expressive Performances. Journal of New Music Research 30(1), 39–58.
5. Dixon, S. and Cambouropoulos, E. (2000). Beat Tracking withMusical Knowledge. In Proceedings of the 14th European Conference on Artificial Intelligence (ECAI- 2000), IOS Press, Amsterdam.
6. Dixon, S. (2001). An Empirical Comparison of Tempo Trackers. In Proceedings of the VIII Brazilian Symposium on Computer Music (SBCM’01), Fortaleza, Brazil.
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.
8. Friberg, A. (1995). A Quantitative Rule System for Musical Performance. Doctoral Dissertation, Royal Institute of Technology (KTH), Stockholm, Sweden.
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.
10. Hunter, L. (ed.) (1993). Artificial Intelligence and Molecular Biology. Menlo Park, CA: AAAI Press.
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.
12. Kohonen, T. (2001). Self-Organizing Maps, 3rd edition. Berlin: Springer Verlag.
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.
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.
15. Ludl, M. and Widmer, G. (2002). Towards a Simple Clustering Criterion Based on Minimum Length Encoding. In Proceedings of the 13th European Conference on Machine Learning (ECML’02), Helsinki. Berlin: Springer Verlag.
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.
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.
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.
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.
20. Palmer, C. (1988). Timing in Skilled Piano Performance. Ph.D. Dissertation, Cornell University.
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.
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.
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.
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.
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.
26. Sundberg, J. (1993). How Can Music Be Expressive? Speech Communication 13, 239–253.
27. Todd, N. (1989). Towards a Cognitive Theory of Expression: The Performance and Perception of Rubato. Contemporary Music Review, vol. 4, pp. 405–416.
28. Todd, N. (1992). The Dynamics of Dynamics: A Model of Musical Expression. Journal of the Acoustical Society of America 91, pp.3540–3550.
29. Valdés-Pérez, R.E. (1995). Machine Discovery in Chemistry: New Results. Artificial Intelligence 74(1), 191–201.
30. Valdés-Pérez, R.E. (1996). A New Theorem in Particle Physics Enabled by Machine Discovery. Artificial Intelligence 82(1-2), 331–339.
31. Valdés-Pérez, R.E. (1999). Principles of Human-Computer Collaboration for Knowledge Discovery in Science. Artificial Intelligence 107(2), 335-346.
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.
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.
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.
35. Widmer, G. (2002). Machine Discoveries: A Few Simple, Robust Local Expression Principles. Journal of New Music Research 31(1) (in press).
36. Widmer, G., and Tobudic, A. (2002). Playing Mozart by Analogy: Learning Multilevel Timing and Dynamics Strategies. Journal of New Music Research (to appear).
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.
38. Zwicker, E. and Fastl, H. (2001). Psychoacoustics. Facts and Models. Springer Series in Information Sciences, Vol.22. Berlin: Springer Verlag.