GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning
Metadata
Show full item recordEditorial
MDPI
Materia
Automatic musical composition Metaprogramming Procedural representation of music Artificial creativity GenoMus
Date
2022-08-19Referencia bibliográfica
López-Montes, J.; Molina-Solana, M.; Fajardo,W. GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning. Appl. Sci. 2022, 12, 8322. [https://doi.org/10.3390/app12168322]
Sponsorship
FEDER/Junta de Andalucia A.TIC.244.UGR20 Spanish Government; European Commission PID2021-125537NA-I00Abstract
We present GenoMus, a new model for artificial musical creativity based on a procedural
approach, able to represent compositional techniques behind a musical score. This model aims to
build a framework for automatic creativity, that is easily adaptable to other domains beyond music.
The core of GenoMus is a functional grammar designed to cover a wide range of styles, integrating
traditional and contemporary composing techniques. In its encoded form, both composing methods
and music scores are represented as one-dimensional arrays of normalized values. On the other
hand, the decoded form of GenoMus grammar is human-readable, allowing for manual editing
and the implementation of user-defined processes. Musical procedures (genotypes) are functional
trees, able to generate musical scores (phenotypes). Each subprocess uses the same generic functional
structure, regardless of the time scale, polyphonic structure, or traditional or algorithmic process
being employed. Some works produced with the algorithm have been already published. This highly
homogeneous and modular approach simplifies metaprogramming and maximizes search space. Its
abstract and compact representation of musical knowledge as pure numeric arrays is optimized for
the application of different machine learning paradigms.