A methodology for procedural piano music composition with mood templates using genetic algorithms

ROCHA DE AZEVEDO SANTOS, Luisa, SILLA JR., Carlos and DA COSTA ABREU, Marjory (2021). A methodology for procedural piano music composition with mood templates using genetic algorithms. In: 11th International Conference of Pattern Recognition Systems (ICPRS 2021). IET, 1-6.

[img]
Preview
PDF
paper_music_generator_IA.pdf - Accepted Version
All rights reserved.

Download (300kB) | Preview
Official URL: https://ieeexplore.ieee.org/document/9568991
Link to published version:: https://doi.org/10.1049/icp.2021.1435

Abstract

Creating music in an automatic way has been studied since the beginning of artificial intelligence. One of the biggest obstacles of music generation is the vagueness and subjectivity of the mood or emotion transmitted by a music piece. In this work, we experiment with the generation of piano music using template pieces, represented in MIDI format, as a mood directive. We generated a population of random pieces for templates of two opposing moods - happy and sad - and evolved them with a genetic algorithm until their intended mood was close enough to their respective templates. The fitness function that we implemented uses MIDI statistical features to calculate the distance between the given piece and the template. The generated music pieces were evaluated by human listeners thorough a questionnaire. This evaluation has shown that the generated music pieces were able to express the same mood as the template. However, they still sounded computer-generated, probably due to the lack of rhythm regularity and synchronicity.

Item Type: Book Section
Identification Number: https://doi.org/10.1049/icp.2021.1435
Page Range: 1-6
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 22 Jan 2021 17:10
Last Modified: 26 Oct 2021 14:00
URI: https://shura.shu.ac.uk/id/eprint/28011

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics