Data-based melody generation through multi-objective evolutionary computation

Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte [FPU fellowship AP2012-0939]; and the Spanish Ministerio de Economía y Competitividad project TIMuL supported by UE FEDER funds [No. TIN2013–48152–C2–1–R]

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

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.FEDER/Junta de Andalucia A.TIC.244.UGR20 Spanish GovernmentEuropean Commission PID2021-125537NA-I0