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Sound Cells in Genetic Improvisation: An Evolutionary Model for Improvised Music

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Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12103))

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.

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Notes

  1. 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. 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. 3.

    For a short documentation of the recording setting see https://vimeo.com/150357914.

  4. 4.

    https://github.com/bastustrump/genimpro.

  5. 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. 6.

    In GenImpro the Biopython package is integrated [7].

  7. 7.

    Examples of an hermeneutic interpretation of intra-improvisational results are presented on http://www.genimpro.net/intra-improvisational.

  8. 8.

    An interactive version of this visualisation can be found on http://www.genimpro.net.

  9. 9.

    http://www.genimpro.net/index.htm?recordingID=201.

  10. 10.

    http://www.genimpro.net/index.htm?recordingID=247.

  11. 11.

    The term metagenerativity is closely related to the concept of musical metacreation [28], which, however, is restricted to a subsection of artificial creativity. The metagenerative level can be regarded as the superordinate regulator of individual generative processes [8].

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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

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  • DOI: https://doi.org/10.1007/978-3-030-43859-3_13

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