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Music Performer Verification Based on Learning Ensembles

  • Efstathios Stamatatos
  • Ergina Kavallieratou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3025)

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.

Keywords

Base Classifier Learn Ensemble Music Performance Speaker Verification Musical Piece 
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.

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References

  1. 1.
    Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 39(1/2), 105–139 (1999)CrossRefGoogle Scholar
  2. 2.
    Blum, A.: Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26(1), 5–23 (1997)CrossRefGoogle Scholar
  3. 3.
    Eisenbeis, R., Avery, R.: Discriminant Analysis and Classification Procedures: Theory and Applications. D.C. Health and Co., Lexington (1972)Google Scholar
  4. 4.
    Fakotakis, N., Tsopanoglou, A., Kokkinakis, G.: A Text-independent Speaker Recognition System Based on Vowel Spotting. Speech Communication 12, 57–68 (1993)CrossRefGoogle Scholar
  5. 5.
    Friberg, A.: Generative Rules for Music Performance: A Formal Description of a Rule System. Computer Music Journal 15(2), 56–71 (1991)CrossRefMathSciNetGoogle Scholar
  6. 6.
    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)zbMATHCrossRefGoogle Scholar
  7. 7.
    Palmer, C.: On the Assignment of Structure in Music Performance. Music Perception 14, 23–56 (1996)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic Text Categorization in Terms of Genre and Author. Computational Linguistics 26(4), 471–495 (2000)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Stamatatos, E.: Quantifying the Differences Between Music Performers: Score vs. Norm. In: Proc. of the International Computer Music Conference, pp. 376–382 (2002)Google Scholar
  12. 12.
    Widmer, G.: Using AI and Machine Learning to Study Expressive Music Performance: Project Survey and First Report. AI Communications 14, 149–162 (2001)zbMATHGoogle Scholar
  13. 13.
    Widmer, G.: Discovering Simple Rules in Complex Data: A Meta-learning Algorithm and Some Surprising Musical Discoveries. Artificial Intelligence 146(2), 129–148 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Efstathios Stamatatos
    • 1
  • Ergina Kavallieratou
    • 1
  1. 1.Dept. of Audio and Musical Instrument TechnologyT.E.I. of Ionian IslandsLixouri

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