Abstract
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 http://sig-ma.de/wp-content/uploads/2017/01/JazzDrumPatterns.zip.).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
MIDI (Musical Instrument Digital Interface) is a technical standard that allows electronic musical instruments, computers, and other related devices to share musical information with one another. For detailed information, see [12].
References
Aebersold, J.: Volume 1 - How to Play Jazz & Improvise. Jamey Aebersold Jazz (1967) (Jamey Aebersold Play-A-Long Series)
Hughes, C.: Learn Jazz Standards (2010). http://www.learnjazzstandards.com/about/. Accessed 2 Nov 2016
Gannon, P.: Band-in-a-Box. PG Music Inc., Hamilton (1990)
Biolcati, M.: iReal Pro. Technimo LLC, New York (2008)
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)
Lewis, G.E.: Too many notes: computers, complexity and culture in voyager. Leonardo Music J. 10, 33–39 (2000)
Yee-King, M.J.: The evolving drum machine. In: MusicAL 2007 Proceedings (2007). http://cmr.soc.plymouth.ac.uk/Musical2007/proceedings.htm. Accessed 5 Nov 2016
Hoover, A.K., Stanley, K.O.: Exploiting functional relationships in musical composition. Connection Sci. 21(2–3), 227–251 (2009)
Tokui, N., Iba, H.: Music Composition with Interactive Evolutionary Computation. Graduate School of Engineering, The University of Tokyo (2001). http://www.generativeart.com/on/cic/2000/ga2000-tokui.htm. Accessed 29 Oct 2016
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)
Dostál, M.: Genetic algorithms as a model of musical creativity - on generating of a human-like rhythmic accompaniment. Comput. Inform. 22, 321–340 (2005)
MMA, MIDI Manufacturers Association: General MIDI 1, 2 and Lite Specifications (1991). https://www.midi.org/specifications/category/gm-specifications. Accessed 6 Nov 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ostermann, F., Vatolkin, I., Rudolph, G. (2017). Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-55750-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55749-6
Online ISBN: 978-3-319-55750-2
eBook Packages: Computer ScienceComputer Science (R0)