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International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)

EvoMUSART 2021: Artificial Intelligence in Music, Sound, Art and Design pp 3–19Cite as

Sculpture Inspired Musical Composition

One Possible Approach

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12693)

Abstract

In this paper, we present an inspirational system that takes a 3D model of a sculpture as starting point to compose music. It is considered that cross-domain mapping can be an approach to model inspiration. Our approach does not consider the interpretation of the sculpture but rather looks at it abstractly. The results were promising: the majority of the participants gave a classification of 4 out of 5 to the preferred interpretations of the compositions and related them to the respective sculpture. This is a step to a possible model for inspiration.

Keywords

  • Computational creativity
  • Inspiration
  • Genetic algorithm
  • Sculpture
  • Musical composition

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Acknowledgments

This work was supported by Fundação para a Ciência e a Tecnologia, under project UIDB/50021/2020 We thank all comments and suggestions from Nuno Correia and Rui Pereira Jorge.

Author information

Authors and Affiliations

  1. INESC ID, Lisbon, Portugal

    Francisco Braga & H. Sofia Pinto

  2. Instituto Superior Técnico, Lisbon, Portugal

    Francisco Braga & H. Sofia Pinto

Authors
  1. Francisco Braga
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  2. H. Sofia Pinto
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Corresponding authors

Correspondence to Francisco Braga or H. Sofia Pinto .

Editor information

Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Juan Romero

  2. University of Coimbra, Coimbra, Portugal

    Tiago Martins

  3. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

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Braga, F., Pinto, H.S. (2021). Sculpture Inspired Musical Composition. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-72914-1_1

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