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Article

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

1
Department Computer Science and AI, Universidad de Granada, E-18071 Granada, Spain
2
Royal Conservatory of Music Victoria Eugenia of Granada, E-18001 Granada, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Philippe Esling
Appl. Sci. 2022, 12(16), 8322; https://doi.org/10.3390/app12168322
Received: 30 June 2022 / Revised: 15 August 2022 / Accepted: 16 August 2022 / Published: 19 August 2022
(This article belongs to the Special Issue Advances in Computer Music)
Augmented musical creativity, computational musicology, and procedural representation of music for machine learning algorithms.
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. View Full-Text
Keywords: automatic musical composition; metaprogramming; procedural representation of music; artificial creativity; GenoMus automatic musical composition; metaprogramming; procedural representation of music; artificial creativity; GenoMus
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MDPI and ACS Style

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

AMA Style

López-Montes J, Molina-Solana M, Fajardo W. GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning. Applied Sciences. 2022; 12(16):8322. https://doi.org/10.3390/app12168322

Chicago/Turabian Style

López-Montes, José, Miguel Molina-Solana, and Waldo Fajardo. 2022. "GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning" Applied Sciences 12, no. 16: 8322. https://doi.org/10.3390/app12168322

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