| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560797 |
| ISSN: | 1405-5546 |
| Autores: | Lara, Adriana1 Guzmán, Giovanni2 Vilchis, Natan1 |
| Instituciones: | 1Instituto Politécnico Nacional, Escuela Superior de Física y Mateméticas, México 2Instituto Politécnico Nacional, Centro de Investigación en Computación, México |
| Año: | 2023 |
| Periodo: | Abr-Jun |
| Volumen: | 27 |
| Número: | 2 |
| Paginación: | 401-413 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | This work proposes a mathematical model for computer-aided music composition as a multi-objective optimization problem. This work aims to create a framework to automatically generate a set of songs with two melodies by combining a genetic algorithm with machine learning. Musical patterns were studied 16, 6, 18, 2 to simplify them and apply them for the construction of the optimization model. This work uses recent emotional music theory to construct the optimization problem 11. Three conflicting objective functions represent the desired characteristics of the melody to be created: (1) song happiness, (2) song minimalism, and (3) song genre. Two of these objectives are analytically designed, fulfilling well-studied features like those in 14, 25, 11. The third objective function was developed using a machine learning model like in 5, 8, 27. The software JSymbolic is used 15 for extracting features in real-time and getting the score with the machine learning model trainer in the present work. The results obtained by this work can be listened to by test examples presented in a video format. |
| Keyword: | Music composition, Multiobjective optimization, Evolutionary music |
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