Abstract
Musical improvisation and biological evolution are similarly based on the principles of unpredictability and adaptivity. Within this framework, this research project examines whether and how structures of evolutionary developmental logic can be detected and described in free improvisation. The underlying concept of improvisation is participative in nature and, in this light, contains similar generative strategies as there are in evolutionary processes. Further implications of the theory of evolution for cultural development in the concept of memetics and the form of genetic algorithms build an interdisciplinary network of different theories and methodologies, from which the proposed model of genetic improvisation emerges.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Memetics has developed the notion of “co-adaptive memplexes” for this mode of mutual selection pressure, which can be applied to the interaction of the sound cell lineages. Dawkins writes: “memes, like genes, are selected against the background of other memes in the meme pool. The result is that of mutually compatible memes – coadapted meme complexes or memeplexes – are found cohabiting in individual brains.” [2].
- 2.
The question of a suitable segmentation of the sound material for an analytical access proves to be very complex in detail. Dawkins already hints at the problem of blurring in the segmentation of a meme: “So far I have talked about memes as though it is obvious what a single unit-meme consited of. But of course it is far from obvious. I have said it is a meme, but what about a symphony: how many memes is that? Is each movement one meme, each recognizable phrase of melody, each bar, each chord, or what?” [9]. The model of genetic improvisation follows Jan’s considerations on composed music, according to which small units provide easier ways to connect to a “intact imitation” [19].
- 3.
For a short documentation of the recording setting see https://vimeo.com/150357914.
- 4.
- 5.
The most common methods are (1.) UPGMA unweighted pair-group method using arithmetic averages or neighbor-joining [31] for calculating an initial tree using a distance matrix [12], (2.) the parsimony principle, in which several possible trees are named after the number of trees and (3.) Maximum Likelihood, which selects the most probable of random trees.
- 6.
In GenImpro the Biopython package is integrated [7].
- 7.
Examples of an hermeneutic interpretation of intra-improvisational results are presented on http://www.genimpro.net/intra-improvisational.
- 8.
An interactive version of this visualisation can be found on http://www.genimpro.net.
- 9.
- 10.
- 11.
References
Biles, J.: GenJam: a genetic algorithm for generating Jazz Solos. In: Proceedings of the 1994 International Computer Music Conference. University of Michigan Library (1994)
Blackmore, S.J.: The Meme Machine. Oxford University Press, Oxford (1999)
Boden, M.A.: The Creative Mind: Myths & Mechanisms. Basic Books (1991)
Bogdanov, D., et al.: ESSENTIA: an audio analysis library for music information retrieval. In: International Society for Music Information Retrieval Conference (ISMIR 2013), pp. 493–498 (2013)
Borgo, D.: Sync or Swarm: Improvising Music in a Complex Age. Continuum (2005)
Chomsky, N.: Aspects of the Theory of Syntax. MIT Press, Cambridge (1969)
Cock, P.J.A., et al.: Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25(11), 1422–1423 (2009). https://doi.org/10.1093/bioinformatics/btp163
Dahlstedt, P.: Thoughts on creative evolution: a meta-generative approach to composition. Contemp. Music Rev. 28(1), 43–55 (2009). https://doi.org/10.1080/07494460802664023
Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1978)
Dennett, D.: The interpretation of texts, people and other artifacts. Philos. Phenomenol. Res. 50, 177–194 (1990)
Dennett, D.: Darwin’s Dangerous Idea: Evolution and the Meanings of Life. Simon & Schuster (1995)
Farris, J.S.: Estimating phylogenetic trees from distance matrices. Am. Nat. 106(951), 645–668 (1972). https://doi.org/10.1086/282802
Ferretti, S.: On the modeling of musical solos as complex networks. Inf. Sci. (2016). https://doi.org/10.1016/j.ins.2016.10.007
Fitch, W.M., Margoliash, E.: Construction of phylogenetic trees. Science 155(3760), 279–284 (1967). https://doi.org/10.1126/science.155.3760.279
Frieler, K., Lothwesen, K., Schütz, M.: The ideational flow: evaluating a new method for Jazz improvisation analysis. In: Proceedings of the 12th International Conference on Music Perception and Cognition and the 8th Triennial Conference of the European Society for the Cognitive Sciences of Music (2012)
Frieler, K., Pfleiderer, M., Zaddach, W.G., Abeßer, J.: Midlevel analysis of monophonic Jazz Solos: a new approach to the study of improvisation. Musicae Scientiae (2016). https://doi.org/10.1177/1029864916636440
Glaser, B.G., Strauss, A.L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Publishing Company (1967)
Hennig, W.: Phylogenetic Systematics. University of Illinois Press (1999)
Jan, S.: The Memetics of Music: A Neo-Darwinian View of Musical Structure and Culture. Ashgate (2007)
Johnson-Laird, P.N.: How Jazz musicians improvise. Music Percept.: Interdiscip. J. 19(3), 415–442 (2002). https://doi.org/10.1525/mp.2002.19.3.415
Lartillot, O.: A musical pattern discovery system founded on a modeling of listening strategies. Comput. Music J. 28(3), 53–67 (2004). https://doi.org/10.1162/0148926041790694
Lartillot, O., Ayari, M.: Comprehensive and complex modeling of structural understanding, studied of an experimental improvisation. In: Proceedings of the 12th International Conference on Music Perception and Cognition (ICMPC) and the 8th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM) (2012). http://cms.unige.ch/fapse/neuroemo/pdf/ArticleLartillotAyari.pdf
Lerdahl, F., Jackendorff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge (1996)
McAdams, S.: Perspectives on the contribution of timbre to musical structure. Comput. Music J. 23(3), 85–102 (1999). http://www.mitpressjournals.org/doi/pdf/10.1162/014892699559797
McCormack, J.: Open problems in evolutionary music and art. In: Rothlauf, F. (ed.) Applications of Evolutionary Computing: EvoWorkshops2005, EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Lausanne, Switzerland (2005)
Miłkowski, M.: Is evolution algorithmic? Minds Mach. 19(4), 465–475 (2009). https://doi.org/10.1007/s11023-009-9170-6
Miranda, E. (ed.): A-Life for Music: Music and Computer Models of Living Systems. A-R Editions (2011)
Pasquier, P., Eigenfeldt, A., Bown, O., Dubnov, S.: An introduction to musical metacreation. Comput. Entertain. 14(2), 2:1–2:14 (2017). https://doi.org/10.1145/2930672
Pressing, J.: Improvisation: methods and models. In: Sloboda, J.A. (ed.) Generative Processes in Music: The Psychology of Performance, Improvisation, and Composition, pp. 129–178. Oxford University Press, Oxford (1988)
Rahaim, M.: What else do we say when we say “Music Evolves?”. World Music 48(3), 29–41 (2006)
Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4), 406–425 (1987). http://mbe.oxfordjournals.org/content/4/4/406.short
Schenker, H.: Neue musikalische Theorien und Phantasien. Band 3: Der freie Satz. Universal-Edition (1935)
Tomlinson, G.: A Million Years of Music: The Emergence of Human Modernity. ZoneBooks (2015)
Trump, S.: The evolution of sound cells. pivot point for the analysis and creation of musical improvisation. In: Pasquier, P., Eigenfeldt, A., Bown, O. (eds.) Proceedings of the 4th International Workshop on Musical Metacreation (MUME 2016) (2016). http://musicalmetacreation.org/mume2016/proceedings/trump_the_evolution.pdf
Wallin, N., Merker, B., Brown, S. (eds.): The Origins of Music. MIT Press/A Bradford Book (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Trump, S. (2020). Sound Cells in Genetic Improvisation: An Evolutionary Model for Improvised Music. In: Romero, J., Ekárt, A., Martins, T., Correia, J. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2020. Lecture Notes in Computer Science(), vol 12103. Springer, Cham. https://doi.org/10.1007/978-3-030-43859-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-43859-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43858-6
Online ISBN: 978-3-030-43859-3
eBook Packages: Computer ScienceComputer Science (R0)