Advertisement

Automatic Polyphonic Music Composition Using the EMILE and ABL Grammar Inductors

  • David Ortega-Pacheco
  • Hiram Calvo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

In this paper we present a method for automatic polyphonic music composition using the ABL and EMILE grammar inductors. To evaluate the performance of the EMILE and ABL engines we use a voting classification scheme based on TF.IDF weighting—we show a novel adaptation of the n-gram concept for music classification. We performed experiments with six musical MIDI collections from a different classical music composer each (Bach, Chopin, Liszt, Schubert, Mozart and Haydn). For each composer we applied our method to obtain five new polyphonic music compositions, and then we tested the membership of the new compositions with regard to every composer. We found that new compositions have similar membership to the set of composer styles than natural compositions. We conclude that our method is capable of creating new, relatively original compositions in the same musical style of each author.

Keywords

Grammar Induction Automatic Music Composition EMILE Grammar Inductor ABL Grammar Inductor M-Grams TF.IDF weighting 

References

  1. 1.
    Black, J.A., Ranjan, N.: Automated Event Extraction from Email. Final Report of CS224N/Ling237 course in Stanford (2004)Google Scholar
  2. 2.
    Alcázar, C., Pedro, P., Ruiz, E.V.: A Study of Grammatical Inference Algorithms in Automatic Music Composition and Musical Style Recognition. In: Workshop on Automata Induction, Grammatical Inference, and Language Acquisition. The Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, TN, USA (1997)Google Scholar
  3. 3.
    Eric, D.: Extension of the EMILE algorithm for inductive learning of context-free grammars for natural languages. Master’s Thesis, University of Dortmund, UK (1997)Google Scholar
  4. 4.
    Manning Christhopher, D., Hinrich, S.: Foundations of Statistical Natural Language Processing, pp. 541–544. MIT Press, England (2000) (second printing)Google Scholar
  5. 5.
    David, O.-P., Calvo, H.: Music Composition using the EMILE Grammar Inductor. In: Gelbukh, A., Kuri, A. (eds.) Advances in Artificial Intelligence and Applications, Research in Computing Science, pp. 341–351 (2007)Google Scholar
  6. 6.
    Selfridge-Field, Eleanor: Beyond MIDI, the Handbook of Musical Codes, pp. 41–72. The MIT Press, Cambridge (1997)Google Scholar
  7. 7.
    van Zaanen, M.: ABL: Alignment-Based Learning. In: Proceedings of the 18th International Conference on Computational Linguistics COLING, pp. 961–967 (2000)Google Scholar
  8. 8.
    van Zaanen, M.: Bootstrapping structure using similarity. In: Monachesi, P. (ed.) Computational Linguistics in the Netherlands, pp. 235–245 (1999)Google Scholar
  9. 9.
    van Zaanen, M.: Bootstrapping syntax and recursion using alignment-based learning. In: Procs. 17th International Conference on Machine Learning ICML, pp. 1063–1070 (2000)Google Scholar
  10. 10.
    Vervoorte, M.: Emile 4.1.7 User Guide. University of Amsterdam (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David Ortega-Pacheco
    • 1
  • Hiram Calvo
    • 1
  1. 1.Centro de Investigación en Computación, Instituto Politécnico NacionalMéxico, D. F.México

Personalised recommendations