Music Composition Based on Linguistic Approach

  • Horacio Alberto García Salas
  • Alexander Gelbukh
  • Hiram Calvo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6437)


Music is a form of expression. Since machines have limited capabilities in this sense, our main goal is to model musical composition process, to allow machines to express themselves musically. Our model is based on a linguistic approach. It describes music as a language composed of sequences of symbols that form melodies, with lexical symbols being sounds and silences with their duration in time. We determine functions to describe the probability distribution of these sequences of musical notes and use them for automatic music generation.


Affective computing evolutionary systems evolutionary matrix generative music generative grammars 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Biles, J.A.: GenJam: Evolution of a jazz improviser. In: Source. Creative evolutionary systems. Section: Evolutionary music, pp. 165–187. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  2. 2.
    Birchfield, D.: Generative model for the creation of musical emotion, meaning and form. In: Source International Multimedia Conference. Proceedings of the 2003 ACM SIGMM Workshop on Experiential Telepresence, pp. 99–104. ACM, New York (2003)CrossRefGoogle Scholar
  3. 3.
    Blackburm, S., DeRoure, D.: A tool for content based navigation of music. In: Source International Multimedia Conference. Proceedings of the Sixth ACM International Conference on Multimedia, Bristol, United Kingdom, pp. 361–368 (1998)Google Scholar
  4. 4.
    Blackwell, T.: Swarming and Music. Evolutionary Computer Music. In: Subject Collection: Informática. In SpringerLink since, pp. 194–217. Springer, London ( October 12, 2007)Google Scholar
  5. 5.
    Bulmer, M.: Music From Fractal Noise. In: Proceedings of the Mathematics 2000 Festival, Melbourne, University of Queensland January 10–13 (2000)Google Scholar
  6. 6.
    Eck, D., Schmidhuber, J.: A First Look at Music Composition using LSTM Recurrent Neural Networks. Source Technical Report: IDSIA-07-02. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale (2002)Google Scholar
  7. 7.
    Galindo Soria, F.: Sistemas Evolutivos: Nuevo Paradigma de la Informática. In: Memorias XVII Conferencia Latinoamericana de Informática, Caracas Venezuela (July 1991)Google Scholar
  8. 8.
    Galindo Soria, F.: Enfoque Lingüístico. Instituto Politécnico Nacional UPIICSA ESCOM (1994)Google Scholar
  9. 9.
    Galindo Soria, F.: Teoría y Práctica de los Sistemas Evolutivos. Mexico. Editor Jesús Manuel Olivares Ceja (1997)Google Scholar
  10. 10.
    Galindo Soria, F.: Matrices Evolutivas. La Revista Científica, ESIME del IPN, #8 de 1998. In: Cuarta Conferencia de Ingeniería Eléctrica CIE/98, CINVESTAV-IPN, Cd. de México, pp. 17–22 (September 1998)Google Scholar
  11. 11.
    Hild, H., Feulner, J., Menzel, W.: Harmonet: A Neural Net for Harmonizing Chorales in the Style of J.S.Bach. In: Lippmann, R.P., Moody, J.E., Touretzky, D.S. (eds.) Neural Information Processing 4 (NIPS 4), pp. 267–274. Morgan Kaufmann Universität Karlsruhe, GermanyGoogle Scholar
  12. 12.
    Järveläinen, H.: Algorithmic Musical Composition. April 7, Tik-111.080 Seminar on content creation Art@Science. Helsinki University of Technology Laboratory of Acoustics and Audio Signal Processing (2000)Google Scholar
  13. 13.
    Kosina, K.: Music Genre Recognition. Diplomarbeit. Eingereicht am Fachhochschul-Studiengang. Mediente Chnik und Design in Hagenberg (June 2002)Google Scholar
  14. 14.
    Maarten, G.J.A., López, M.R.: A Case Based Approach to Expressivity-Aware Tempo Transformation. Source Machine Learning 65(2-3), 411–437 (2006)CrossRefGoogle Scholar
  15. 15.
    Miranda, E.R., Jesus, L.A., Barros, B.: Music Knowledge Analysis: Towards an Efficient Representation for Composition. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 331–341. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Minsky, M.: Music, Mind, and Meaning. Computer Music Journal 5(3) (Fall 1981)Google Scholar
  17. 17.
    Namunu, M., Changsheng, X., Mohan, S.K., Shao, X.: Content-based music structure analysis with applications to music semantics understanding. In: Source International Multimedia Conference. Proceedings of the 12th Annual ACM International Conference on Multimedia. Technical session 3: Audio Processing, pp. 112–119. ACM, New York (2004)Google Scholar
  18. 18.
    Ortega, A.P., Sánchez, A.R., Alfonseca, M.M.: Automatic composition of music by means of Grammatical Evolution. In: ACM SIGAPL APL, vol. 32(4), pp. 148–155. ACM, New York (June 2002)Google Scholar
  19. 19.
    Papadopoulos, G., Wiggins, G.: AI Methods for Algorithmic Composition: A Survey, a Critical View and Future Prospects. In: AISB Symposium on Musical Creativity. School of Artificial Intelligence, Division of Informatics, pp. 110–117. University of Edinburgh, Edinburgh (1999)Google Scholar
  20. 20.
    Picard, R.W., Vyzas, E., Healey, J.: Toward Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10) (October 2001)Google Scholar
  21. 21.
    Todd, P.M., Werner, G.M.: Frankensteinian Methods for Evolutionary Music Composition. In: Griffith, N., Todd, P.M. (eds.) Musical Networks, p. 385. MIT Press, Cambridge (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Horacio Alberto García Salas
    • 1
  • Alexander Gelbukh
    • 1
  • Hiram Calvo
    • 1
    • 2
  1. 1.Natural Language Laboratory, Computing Research CenterNational Polytechnic InstituteDFMexico
  2. 2.Computational Linguistics LaboratoryNara Institute of Science and TechnologyNaraJapan

Personalised recommendations