Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos

  • Fabian Ostermann
  • Igor Vatolkin
  • Günter Rudolph
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10198)


The learning of improvisation in jazz and other music styles requires years of practice. For music scholars which do not play in a band, technical solutions for automatic generation of accompaniment on home computers are very helpful. They may support the learning process and significantly improve the experience to play with other musicians. However, many up-to-date approaches can not interact with a solo player, generating static or random patterns without a direct musical dialogue between a soloist and accompanying instruments. In this paper, we present a novel system for the generation of drum patterns based on an evolutionary algorithm. As the main extension to existing solutions, we propose a set of musically meaningful jazz-related rules for the real-time validation and adjustment of generated drum patterns. In the evaluation study, musicians agreed that the system can be successfully used for learning of jazz improvisation and that the wide range of parameters helps to adapt the response of the virtual drummer to the needs of individual scholars (Examples of generated music are available at


Evolutionary music generation Rhythm generation Jazz solo accompaniment 


  1. 1.
    Aebersold, J.: Volume 1 - How to Play Jazz & Improvise. Jamey Aebersold Jazz (1967) (Jamey Aebersold Play-A-Long Series)Google Scholar
  2. 2.
    Hughes, C.: Learn Jazz Standards (2010). Accessed 2 Nov 2016
  3. 3.
    Gannon, P.: Band-in-a-Box. PG Music Inc., Hamilton (1990)Google Scholar
  4. 4.
    Biolcati, M.: iReal Pro. Technimo LLC, New York (2008)Google Scholar
  5. 5.
    Biles, J.A.: GenJam: a genetic algorithm for generating jazz solos. In: Proceedings of the International Computer Music Conference (ICMC 1994), San Francisco, USA, pp. 131–137. International Computer Association (1994)Google Scholar
  6. 6.
    Lewis, G.E.: Too many notes: computers, complexity and culture in voyager. Leonardo Music J. 10, 33–39 (2000)CrossRefGoogle Scholar
  7. 7.
    Yee-King, M.J.: The evolving drum machine. In: MusicAL 2007 Proceedings (2007). Accessed 5 Nov 2016
  8. 8.
    Hoover, A.K., Stanley, K.O.: Exploiting functional relationships in musical composition. Connection Sci. 21(2–3), 227–251 (2009)CrossRefGoogle Scholar
  9. 9.
    Tokui, N., Iba, H.: Music Composition with Interactive Evolutionary Computation. Graduate School of Engineering, The University of Tokyo (2001). Accessed 29 Oct 2016
  10. 10.
    Unemi, T., Nakada, E.: A tool for composing short music pieces by means of breeding. In: Proceedings of the 2001 IEEE Systems, Man and Cybernetics Conference, pp. 3458–3463 (2001)Google Scholar
  11. 11.
    Dostál, M.: Genetic algorithms as a model of musical creativity - on generating of a human-like rhythmic accompaniment. Comput. Inform. 22, 321–340 (2005)zbMATHGoogle Scholar
  12. 12.
    MMA, MIDI Manufacturers Association: General MIDI 1, 2 and Lite Specifications (1991). Accessed 6 Nov 2016

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabian Ostermann
    • 1
  • Igor Vatolkin
    • 1
  • Günter Rudolph
    • 1
  1. 1.Fakultät für InformatikTechnische Universität DortmundDortmundGermany

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