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haspie - A Musical Harmonisation Tool Based on ASP

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

In this paper we describe a musical harmonisation and composition assistant based on Answer Set Programming (ASP). The tool takes scores in MusicXML format and annotates them with a preferred harmonisation. If specified, it is also able to complete intentionally blank sections and create new parts of the score that fit with the proposed harmonisation. Both the harmonisation and the completion of blank parts can be seen as constraint satisfaction problems that are encoded in ASP. Although the tool is a preliminary prototype still being improved, its basic functionality already helps to illustrate the appropriateness of ASP for musical knowledge representation, which provides a high degree of flexibility thanks to its relational, declarative orientation and an efficient computation of preferred solutions.

This work has been partially supported by MINECO, Spain (project TIN2013-42149-P), Xunta de Galicia, Spain (projects GPC ED431B 2016/035 and 2016-2019 ED431G/01 for CITIC center) and European Regional Development Fund (ERDF).

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Notes

  1. 1.

    https://musescore.org/.

  2. 2.

    http://potassco.sourceforge.net/.

  3. 3.

    http://web.mit.edu/music21/.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Universidade da Coruña, A Coruña, Spain

    Pedro Cabalar & Rodrigo Martín

Authors
  1. Pedro Cabalar
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  2. Rodrigo Martín
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Corresponding author

Correspondence to Pedro Cabalar .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Cabalar, P., Martín, R. (2017). haspie - A Musical Harmonisation Tool Based on ASP. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_52

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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