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Evolutionary Approach to Melodic Line Harmonization

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13588))

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Abstract

The paper presents a novel evolutionary algorithm (EA) for melodic line harmonization (MLH) - one of the fundamental tasks in music composition. The proposed method solves MLH by means of a carefully constructed fitness function (FF) that reflects theoretical music laws, and dedicated evolutionary operators. A modular design of the FF makes the method flexible and easily extensible. The paper provides a detailed analysis of technical EA implementation, its parameterization, and experimental evaluation. A comprehensive study proves the algorithm’s efficacy and shows that constructed harmonizations are not only technically correct (in line with music theory) but also nice to listen to, i.e. they fulfill aesthetic requirements, as well. The latter aspect is verified and rated by a music expert - a harmony teacher.

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Author information

Authors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Jan Mycka, Adam Żychowski & Jacek Mańdziuk

  2. AGH University of Science and Technology, Krakow, Poland

    Jacek Mańdziuk

Authors
  1. Jan Mycka
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  2. Adam Żychowski
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  3. Jacek Mańdziuk
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Corresponding author

Correspondence to Jacek Mańdziuk .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Mycka, J., Żychowski, A., Mańdziuk, J. (2023). Evolutionary Approach to Melodic Line Harmonization. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-23492-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23491-0

  • Online ISBN: 978-3-031-23492-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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