Experimental evolution of music styles using automatic composition models
Eita Nakamura1 , Hitomi Kaneko2 , Takayuki Itoh3 , Kunihiko Kaneko4
1

Kyoto University 2 Tokyo University of the Arts
3
Ochanomizu University 4 Niels Bohr Instituteeita.nakamura@i.kyoto-u.ac.jp

Abstract
Musical culture is developed through descent-withmodification processes of complex knowledge about music
creation, but how new creation styles evolve is not wellunderstood. To study this problem, we conducted an artificial
evolution experiment involving a population of machinelearning-based automatic composition models. These models
evolved under evaluation by a large number of humanlisteners. We found that adaptive evolution of music styles
can occur when the blending inheritance of high-dimensional
statistics representing composition styles is incorporated into
the generation update process. We also found a significant
difference in musical preferences among listeners, dependingon their musical experience. The results indicate the relevance of multi-parental transmission in the cultural evolutionof music styles, a nontrivial fitness landscape of creationstyles, and listeners’ diverse preferences.

Introduction
Musical cultures develop through iterated processes of inheritance and modification of complex knowledge for creating music. Computational representation of such knowledge has long been studied in the field of automatic music
composition (Nierhaus, 2009; Ebcioğlu, 1990; Pachet andRoy, 2011; Biles, 1994). Recent studies based on machinelearning have shown that musical knowledge represented byhigh-dimensional statistics extracted from music data is effective for generating songs that imitate the features of existing music (Hadjeres et al., 2017; Sturm et al., 2016; Mittalet al., 2021; Agostinelli et al., 2023). However, how newand preferred music creation styles evolve is not well understood. Given the high dimensionality of genotype (knowledge) and phenotype (songs), and the stochastic relation between them, the conditions for adaptive evolution of music styles cannot be easily revealed by data analysis (Mauchet al., 2015; Singh and Nakamura, 2022) or by simple psychological experiments (Salganik et al., 2006).
We conduct an experiment to study the process of music style evolution in a constructive way. The framework issimilar to that of the DarwinTunes experiment (MacCallumet al., 2012), which was based on evolutionary computationt-th generation
composition models
Songgeneration

・・・・・

Song 1Song 2
Song 3

Song 1Song 2
Song 3

Song 1Song 2
Song 3・・・・・Song 1Song 2
Song 3

Evaluation by publicSale rate
Fitness
Selection andinheritanceprimary
secondary

0.72
8.7

0.53
4.9

0.32
2.6

(t+1)-th generation
composition models

・・・・・・・・
・・

0.68
7.7

・・・
・・

Figure 1: Overview of the experimental evolution.
involving human listeners. Instead of using sound generation algorithms as in that experiment, we employ machinelearning-based automatic composition models. These models can generate an indefinite number of songs, simulating
creators who inherit a creation style by learning the statistics
of songs from their cultural parents, rather than inheriting asong directly. We study the effects of blending inheritance,
which mimics the fusion of different creation styles duringtransmission, and selection based on listeners’ evaluationsof the generated songs. In the following, we describe theoverview of the method and the main results; the study will
be presented in full detail in a forthcoming paper.

MethodIn the experiment, the composition models generate
melodies for given sets of chord progressions and lyrics(Fig. 1). We use as a composition model a product-ofexperts model integrating a Markov model describing pitchtransitions and rhythms (Nakamura et al., 2019), and aharmony action model describing the relationship between
melody pitches and chords. This model has approx. 105
probabilistic parameters, which are treated as traits. Depending on the parameterization, a composition model cangenerate melodies in various existing styles, e.g. jazz androck styles, as well as styles different from existing music.
50 composition models are present in each generation andthe population is updated according to the models’ fitness
wn . The fitness is measured by the human listeners’ evaluations of the songs generated by the models for 20 chord pro-Result
Generation updates were performed every few days, and by
the 11th generation, most clans were removed from the population and six clans survived (the predominant clans in thetenth generation are labeled as C1 to C7). The average rating increased over generations, and a musical feature analysis showed considerable changes in the creation style ofsome clans (e.g. C2). An additional experiment was conducted to directly compare the 25 progenitor models, theaverage model, and representative models of clans C1 toC7 in the first and tenth generations. In this experiment,
the models were put on a public automatic composition system (https://creevo-music.com) and a large number of general users evaluated them by random and blindcomparisons. As a result, we observed significant increases
in the sale rates in clans C2 and C7 over generations, indicating the adaptive evolution within these clans (Fig. 2A).
The tenth-generation model of clan C2 was the most popular and had a significantly higher sale rate than that of theaverage model, indicating a nontrivial fitness landscape in
the high-dimensional space of music styles.
Listeners’ preferences were analyzed by their buy rates,
the relative rates of choosing songs created by the predominant seven clans. A factor analysis on the buy rates revealed

A 1.6
Relative sale rate

95% CI

Averagemodel

1.4
1.2
10.8
0.6

1st generation
10th generation

0.4
Progenitors

B

Relative buy rate

gressions with lyrics. A pair of melodies generated by different composition models for the same chord progression
is presented to a listener at a time, and the listener selects
the one he/she prefers. By repeating these random and blind
comparisons many times, we measure the sale rate rn defined as the rate that composition model n is chosen as a preferred one. The fitness is then defined as wn = exp(3 rn ).
About 70 undergraduate and graduate students, including
students in music colleges, participated in the experiment.
After evaluation, 25 highest fitness models are included in
the next generation, and the rest are removed from the population. For each of the newly introduced 25 models, a primary parent and two secondary parents are chosen from thecurrent population with a probability P (n) ∝ wn . The traits
are transmitted from these parents by blending inheritance:
the offspring’s traits θ is given as θ = (1 − u)θ0 + u(θ1 +θ2 )/2, where θ0 and θk (k = 1, 2) are the traits of the primary and secondary parents, respectively, and u ∈ (0, 1/2)
is the oblique transmission coefficient, which is also treatedas a trait transmitted from the primary parent with a random
mutation. We note that this evolutionary process would end
up converging into the average model obtained by averaging
the traits of initial models, if there is no selection pressure.
To initialize the population, we trained 25 composition
models (called progenitors) using a dataset of approx. 104
popular music songs, by statistical learning based on clustering and the maximum likelihood estimation. Each of the50 models in the first generation are then constructed by randomly choosing three progenitors and averaging their traits.

1.2

C1 C2 C3 C4 C5 C6 C7
Clanw/o harmony theory
w/ harmony theory

1.1
1
0.9
0.8

95% CIC1 C2 C3 C4 C5 C6 C7ClanFigure 2: A: Comparison of composition models. B: Dependence of listeners’ preferences on learning experience.
a difference in the preferences between listeners with andwithout formal learning experience of harmony theory. Aresult using the linear discriminant analysis showed a significant difference (p = 1 × 10−4 ) in the buy rates of thesetwo groups (Fig. 2B).

Discussion
The evolution experiment showed that adaptive evolution
occurs through blending inheritance in the high-dimensional
space of music creation style, suggesting the relevance oftransmission from multiple parents in music style evolution.
The present evolutionary process, where the genotype is amachine learning model and the phenotype is a collection ofsongs, is different from that for song evolution by genetic algorithms (Moroni et al., 2000; MacCallum et al., 2012). In
the former case, random mutations of genotype would notwork. The experimental framework of integrating machinelearning and artificial evolution can be applied to realize anintelligent system that can evolve and adapt.
We plan to continue the evolution experiment to study
how the diversity of music styles can evolve and the possibility of discovering new creation styles that people prefer. Since it is difficult to create a completely new creationstyle only by blending inheritance, other modes of mutation
should be studied. It is also important to study the fitness
landscape more thoroughly and in particular analyze its dynamic part such as the frequency-dependent bias (Nakamura
and Kaneko, 2019; Youngblood, 2019).

Acknowledgements
The authors thank Go Shibata, Ryo Nishikimi, Du Yicheng,
Ryotaro Saito, Takehisa Oyama, Masafumi Hashizume, andthe participants of the experiment for their cooperation. Thiswork was supported by JSPS KAKENHI Nos. 21K12187,21K02846, and 22H03661, JST FOREST Program No. JPMJPR226X, and Novo Nordisk Foundation.

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