Abstract
In this paper the problem of music performer verification is introduced. Given a certain performance of a musical piece and a set of candidate pianists the task is to examine whether or not a particular pianist is the actual performer. A database of 22 pianists playing pieces by F. Chopin in a computer-controlled piano is used in the presented experiments. An appropriate set of features that captures the idiosyncrasies of music performers is proposed. Well-known machine learning techniques for constructing learning ensembles are applied and remarkable results are described in verifying the actual pianist, a very difficult task even for human experts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 39(1/2), 105–139 (1999)
Blum, A.: Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26(1), 5–23 (1997)
Eisenbeis, R., Avery, R.: Discriminant Analysis and Classification Procedures: Theory and Applications. D.C. Health and Co., Lexington (1972)
Fakotakis, N., Tsopanoglou, A., Kokkinakis, G.: A Text-independent Speaker Recognition System Based on Vowel Spotting. Speech Communication 12, 57–68 (1993)
Friberg, A.: Generative Rules for Music Performance: A Formal Description of a Rule System. Computer Music Journal 15(2), 56–71 (1991)
Lim, T., Loh, W., Shih, Y.: A Comparison of Prediction Accuracy, Complexity and Training Time of Thirty-Three Old and New Classification Accuracy. Machine Learning 40(3), 203–228 (2000)
Palmer, C.: On the Assignment of Structure in Music Performance. Music Perception 14, 23–56 (1996)
Repp, B.: 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 (1992)
Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic Text Categorization in Terms of Genre and Author. Computational Linguistics 26(4), 471–495 (2000)
Stamatatos, E.: A Computational Model for Discriminating Music Performers. In: Proc. of the MOSART Workshop on Current Research Directions in Computer Music, pp. 65–69 (2001)
Stamatatos, E.: Quantifying the Differences Between Music Performers: Score vs. Norm. In: Proc. of the International Computer Music Conference, pp. 376–382 (2002)
Widmer, G.: Using AI and Machine Learning to Study Expressive Music Performance: Project Survey and First Report. AI Communications 14, 149–162 (2001)
Widmer, G.: Discovering Simple Rules in Complex Data: A Meta-learning Algorithm and Some Surprising Musical Discoveries. Artificial Intelligence 146(2), 129–148 (2003)
Zanon, P., Widmer, G.: Recognition of Famous Pianists Using Machine Learning Algorithms: First Experimental Results. In: Proc. of the 14th Colloquium of Musical Informatics (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stamatatos, E., Kavallieratou, E. (2004). Music Performer Verification Based on Learning Ensembles. In: Vouros, G.A., Panayiotopoulos, T. (eds) Methods and Applications of Artificial Intelligence. SETN 2004. Lecture Notes in Computer Science(), vol 3025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24674-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-24674-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21937-8
Online ISBN: 978-3-540-24674-9
eBook Packages: Springer Book Archive