Please use this identifier to cite or link to this item: http://monografias.ufrn.br/handle/123456789/7714
Title: An analysis of procedural piano music composition with mood templates using genetic algorithms
Authors: Santos, Luísa Rocha de Azevedo
Keywords: Music Composition;Music Emotion;Music Mood;Genetic Algorithm;Procedural Content Generation
Issue Date: 28-Nov-2018
Publisher: Universidade Federal do Rio Grande do Norte
Citation: SANTOS, Luísa Rocha de Azevedo. An analysis of procedural piano music composition with mood templates using genetic algorithms. 2018. 69 f. Trabalho de Conclusão de Curso (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada, Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2018.
Portuguese 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. A questionnaire applied to human listeners showed that the generated pieces could overall meet the objective, which was to express the same mood as the template. However, they still sounded computer-generated, probably due to the lack of rhythm regularity and synchronicity. We believe that the weights in the fitness function still need to be balanced, and that new rhythm features could make the pieces sound more natural.
URI: http://monografias.ufrn.br/jspui/handle/123456789/7714
Other Identifiers: 20180008263
Appears in Collections:Ciência da Computação

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