Evolutionary music: applying evolutionary computation to the art of creating music

Loughran, Róisín; O’Neill, Michael

doi:10.1007/s10710-020-09380-7

Cited by 20 publications

(7 citation statements)

References 62 publications

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“…The latter offers one first response to the dilemma by framing creativity as an outcome of the entire system, and partially by rooting creativity in the uncertain properties of the environment combined with the genetic operations. In this vein, there are many applications of evolutionary or genetic algorithms in musical creativity (Loughran and O'Neill, 2020), for instance, conceptual blending that recombines material to model originality (e.g. Fauconnier and Turner, 2002;Kaliakatsos-Papakostas and Queiroz, 2017;Cope, 2005).…”

Section: Shifting From "How" To "What"mentioning

confidence: 99%

On Creativity, Music’s AI Completeness, and Four Challenges for Artificial Musical Creativity

Rohrmeier

2022

Transactions of the International Society for Music Information Retrieval

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This article explores the notion of human and computational creativity as well as core challenges for computational musical creativity. It also examines the philosophical dilemma of computational creativity as being suspended between algorithmic determinism and random sampling, and suggests a resolution from a perspective that conceives of "creativity" as an essentially functional concept dependent on a problem space, a frame of reference (e.g. a standard strategy, a gatekeeper, another mind, or a community), and relevance. Second, this article proposes four challenges for artificial musical creativity and musical AI: (1) the cognitive challenge that musical creativity requires a model of music cognition, (2) the challenge of the external world, that many cases of musical creativity require references to the external world, (3) the embodiment challenge, that many cases of musical creativity require a model of the human body, the instrument(s) and the performative setting in various ways, (4) the challenge of creativity at the meta-level, that musical creativity across the board requires creativity at the meta-level. Based on these challenges it is argued that the general capacity of music and its creation fundamentally involves general (artificial) intelligence and that therefore musical creativity at large is fundamentally an AI-complete problem.

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Section: Shifting From "How" To "What"mentioning

confidence: 99%

On Creativity, Music’s AI Completeness, and Four Challenges for Artificial Musical Creativity

Rohrmeier

2022

Transactions of the International Society for Music Information Retrieval

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“…GenJam by Biles (1994) is considered the first work applying EC to jazz improvisation. A recent review of EC applied to music composition is Loughran and O'Neill (2020).…”

Section: Evolutionary Computer Musicmentioning

confidence: 99%

Evaluating Creativity in Automatic Reactive Accompaniment of Jazz Improvisation

Ostermann

Vatolkin

Rudolph

2021

Transactions of the International Society for Music Information Retrieval

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Music generating computer programs can support jazz musicians and students during performance and practice, for instance by providing accompaniment for solo improvisation. However, such software typically plays sequences of static precomposed snippets and does not react to the user. In that context, it is hardly possible to determine whether such a system has any of its own creative powers. Within the scope of a user study with 20 participants, we evaluate and compare the mobile application iReal Pro to our own system, the evolutionary automatic and reactive system called 'EAR Drummer' that generates drum patterns as accompaniment to jazz solo improvisation. It adapts its behaviour in real-time by heuristic rules based on music properties derived from the user's melodies. The user-based evaluation is performed by following the standardised procedure for evaluating creative systems (SPECS). The analysis of the results is based on a Linear Mixed Effects Model to consider fixed and random effects on the survey data. The model reveals that our system outperforms iReal Pro in all of SPECS's partial components of creativity and significantly outperforms it for 7 of those 14 components including variety, originality, emotional involvement, and social interaction. Further, it is characterised as "better" and "more interesting" in the user survey. A conflicting observation is that while 70% of the study participants tend to prefer our more "creative" system as support for stage performances, only 40% find it more suitable for practice. Further analysis addresses differences between user groups defined by their played instrument, age, and musical experience.

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“…This special issue contains five surveys that highlight the wide array of active application domains where GP is finding use. These range from "traditional" AI applications like natural language processing [5], through finance [6], music [7], and education [8], to applications in heavy industry [9].…”

Section: Review Of Application Domainsmentioning

confidence: 99%

“…• In "Applications of Genetic Programming to Finance and Economics: Past, Present, and Future" [6] Tony Brabazon et al consider a consistently popular GP domain, but argue that, when considered objectively from outside the GP community, "there has been surprisingly little work published using GP in the mainstream finance academic community or indeed publicly disclosed by industry practitioners" and focuses on what needs to be done to resolve this. • "Evolutionary music: Applying evolutionary computation to the art of creating music" by Loughran and O'Neill [7] notes that computer music arguably started with Ada Lovelace in 1842, long before the invention of the electronic computer. Their review covers not just GP and other EC techniques but includes NEAT and artificial neural networks, including CTRNNs.…”

mentioning

confidence: 99%