Chorale Music Splicing System: An Algorithmic Music Composer Inspired by Molecular Splicing

  • Clelia De Felice
  • Roberto De Prisco
  • Delfina Malandrino
  • Gianluca Zaccagnino
  • Rocco Zaccagnino
  • Rosalba Zizza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9027)


Splicing systems are a formal model of a generative mechanism of words (strings of characters), inspired by a recombinant behavior of DNA. They are defined by a finite alphabet \(\mathcal{A}\), an initial set \(\mathcal{I}\) of words and a set \(\mathcal{R}\) of rules. Many of the studies about splicing systems focused on the properties of the generated languages and their theoretical computational power.

In this paper we propose the use of splicing systems for algorithmic music composition. Although the approach is general and can be applied to many types of music, in this paper, we focus the attention to the algorithmic composition of 4-voice chorale-like music. We have developed a Java implementation of this approach and we have provided an evaluation of the music output by the system.


Genetic Algorithm Cellular Automaton Input Word Algorithmic Music Music Composition 
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.


  1. 1.
    Hiller, L.: Computer music. Sci. Am. 201(6), 737–759 (1959)CrossRefGoogle Scholar
  2. 2.
    Hiller, L., Isaacson, L.: Experimental Music. McGraw-Hill, New York (1959)Google Scholar
  3. 3.
    Cope, D.: Experiments in Musical Intelligence. A-R Editions, Madison (1996)Google Scholar
  4. 4.
    Cope, D.: The Algorithmic Composer. A-R Editions, Madison (2000)Google Scholar
  5. 5.
    Cope, D.: Virtual Music. The MIT Press, Cambridge (2004)Google Scholar
  6. 6.
    Gimenes, C., Miranda, E., Johnson, C.: On the learning stages of an intelligent rhythmic generator. In: Sound and Music Computing, Salerno, Italy, pp. 244–253 (2005)Google Scholar
  7. 7.
    Gimenes, C., Miranda, E., Johnson, C.: Towards an intelligent rhythmic generator based on given examples: a memetic approach. In: Sound and Music Computing, Glasgow, UK (2005)Google Scholar
  8. 8.
    Miranda, E.: On the music of emergent behaviour: what can evolutionary computation bring to the musician? Leonardo 36(1), 55–58 (2003)CrossRefGoogle Scholar
  9. 9.
    Miranda, E.: On the evolution of music in a society of self-taught digital creatures. Digit. Creativity 1(1), 29–42 (2003)CrossRefGoogle Scholar
  10. 10.
    Miranda, E., Kirby, S., Todd, P.: On computational models of the evolution of music: from the origins of musical taste to the emergence of grammars. Contemp. Music Rev. 22(3), 91–111 (2003)CrossRefGoogle Scholar
  11. 11.
    Miranda, E.: Composing Music with Computers. Focal Press, Oxford (2001)Google Scholar
  12. 12.
    Head, T.: Formal language theory and dna: an analysis of the generative capacity of specific recombinant behaviours. Bull. Math. Biol. 49, 737–759 (1987)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Head, T., Păun, G., Pixton, D.: Language theory and molecular genetics: generative mechanisms suggested by dna recombination. In: Rozenberg, G., Salomaa, A. (eds.) Handbook of Formal Languages, vol. 2, pp. 295–360. Springer, Heidelberg (1996)Google Scholar
  14. 14.
    Zizza, R.: Splicing systems. Scholarpedia 5(7), 9397 (2010)CrossRefGoogle Scholar
  15. 15.
    Bonizzoni, P., de Felice, C., Zizza, R.: The structure of reflexive regular splicing languages via schützenberger constants. Theor. Comput. Sci. 334(1–3), 71–98 (2005)CrossRefzbMATHGoogle Scholar
  16. 16.
    Bonizzoni, P., Jonoska, N.: Regular splicing languages must have a constant. In: Mauri, G., Leporati, A. (eds.) DLT 2011. LNCS, vol. 6795, pp. 82–92. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  17. 17.
    Head, T., Pixton, D.: Splicing and regularity. In: Esik, Z., Martín-Vide, C., Mitrana, V. (eds.) Recent Advances in Formal Languages and Applications. SCI, pp. 119–147. Springer, Heidelberg (2006) Google Scholar
  18. 18.
    Kari, L., Kopecki, S.: Deciding whether a regular language is generated by a splicing system. In: Stefanovic, D., Turberfield, A. (eds.) DNA 2012. LNCS, vol. 7433, pp. 98–109. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  19. 19.
    De Prisco, R., Zaccagnino, G., Zaccagnino, R.: Evobasscomposer: a multi-objective genetic algorithm for 4-voice compositions. In: GECCO, pp. 817–818, ACM (2010)Google Scholar
  20. 20.
    Ebcioglu, K.: An expert system for harmonizing four-part chorales. Machine Models of Music, pp. 385–401. MIT Press, Cambridge (1992) Google Scholar
  21. 21.
    Schottstaedt, B.: Automatic species counterpoint. Technical Report, Stanford, STAN-M-19, May 1984Google Scholar
  22. 22.
    Lehmann, D.: Harmonizing melodies in real-time: the connectionist approach. In: Proceedings of the International Computer Music Association, pp. 27–31 (1997)Google Scholar
  23. 23.
    Phon-Amnuaisuk, S.: Composing using heterogeneous cellular automata. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 547–556. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  24. 24.
    Horner, A., Goldberg, D.: Genetic algorithms and computer assisted music composition. Technical report, University of Illinois (1991)Google Scholar
  25. 25.
    Biles, J.A.: Genjam: a genetic algorithm for generating jazz solos. In: International Computer Music Conference, pp. 131–137 (1994)Google Scholar
  26. 26.
    Jacob, B.L.: Composing with genetic algorithms. Technical report, University of Michigan (1995)Google Scholar
  27. 27.
    Horner, A., Ayers, L.: Harmonization of musical progression with genetic algorithms. In: International Computer Music Conference, pp. 483–484 (1995)Google Scholar
  28. 28.
    De Prisco, R., Zaccagnino, R.: An evolutionary music composer algorithm for bass harmonization. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 567–572. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  29. 29.
    McIntyre, R.: Bach in a box: the evolution of four part baroque harmony using the genetic algorithm. In: International Conference on Evolutionary Computation, pp. 852–857 (1994)Google Scholar
  30. 30.
    Wiggins, G., Papadopoulos, G., Amnuaisuk, S., Tuson, A.: Evolutionary methods for musical composition. In: CASYS98 Workshop on Anticipation, Music and Cognition (1998)Google Scholar
  31. 31.
    Piston, W., DeVoto, M.: Harmony. W. W. Norton, New York (1987)Google Scholar
  32. 32.
    Păun, G.: On the splicing operation. Discrete Appl. Math. 70, 57–79 (1996)CrossRefzbMATHMathSciNetGoogle Scholar
  33. 33.
    Pixton, D.: Regularity of splicing languages. Discrete Appl. Math. 69(1–2), 101–124 (1996)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Clelia De Felice
    • 1
  • Roberto De Prisco
    • 1
  • Delfina Malandrino
    • 1
  • Gianluca Zaccagnino
    • 1
  • Rocco Zaccagnino
    • 1
  • Rosalba Zizza
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly

Personalised recommendations