Integrating Machine Learning and Rule-Based Approaches in Symbolic Music Generation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8360
- @InProceedings{Tanaka:2024:BigData,
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author = "Tsubasa Tanaka",
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title = "Integrating Machine Learning and Rule-Based Approaches
in Symbolic Music Generation",
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booktitle = "2024 IEEE International Conference on Big Data
(BigData)",
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year = "2024",
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pages = "3218--3223",
-
month = dec,
-
keywords = "genetic algorithms, genetic programming, Hands,
Constraint handling, Computational modelling, Neural
networks, Training data, Machine learning, Generative
adversarial networks, Rhythm, Creativity, music
composition, computer-assisted composition, rule-based
approach, constraint satisfaction, generative
adversarial network",
-
ISSN = "2573-2978",
-
DOI = "
doi:10.1109/BigData62323.2024.10825900",
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abstract = "When composing a piece of artistic or experimental
music, a composer often tries to create a new style of
music that differs from existing styles. This
contradicts the idea of conventional
machine-learning-based generation systems because they
learn from existing music pieces and imitate their
styles. Therefore, generating music beyond the assumed
range of styles would be difficult for these systems.
For example, to generate atonal music by learning from
tonal music would be difficult. On the other hand, in
the context of computer-assisted composition,
especially in classical contemporary music, rule-based
approaches based on constraint programming or genetic
programming have been relatively successful. With these
approaches, composers can define original musical
styles by designing rules in the form of constraints or
fitness functions. However, the difficulty of these
approaches is that composers must program every detail
of the music, which is extremely burdensome. As an
early step to creating a computer-assisted composition
system that overcomes both difficulties, this paper
proposes a new neural network model that integrates the
style definition capability of rule-based approaches
and the automation power of machine learning. The key
idea is to generalise the GAN discriminator to
repurpose it as a constraint solver beyond the GAN's
usual function of imitating the original. In addition,
as a proof of concept, this paper shows that the
proposed model can actually output solutions satisfying
some musical rules that are not necessarily relevant to
the dataset.",
-
notes = "Also known as \cite{10825900}",
- }
Genetic Programming entries for
Tsubasa Tanaka
Citations