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A Learning Approach to Hierarchical Features for Automatic Music Composition

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Intelligent Data Analysis and Applications (ECC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 535))

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Abstract

Artificial Evolution has shown great potential in the musical domain. One task in which Evolutionary techniques have shown special promise is the automatic music composition. This article describes the development of an algorithm for generating tonal melodies. The method employed does not entail any preset rule with respect to the musical grammar. It is based on a self-learning model that combines a Markov process, for the creation of concatenation rules of various sounds, with the Viterbi algorithm, for compliance with the musical syntax. The article is going to demonstrate the effectiveness of the method by means of some examples of its production and is going to indicate ways to improve the method.

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Author information

Authors and Affiliations

  1. Department of Technology, Music Academy “Studio Musica”, Treviso, Italy

    Michele Della Ventura

Authors
  1. Michele Della Ventura
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Corresponding author

Correspondence to Michele Della Ventura .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology , Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava , Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Tien-Wen Sung

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Xiao Dong Wang

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Ventura, M.D. (2017). A Learning Approach to Hierarchical Features for Automatic Music Composition. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-48499-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48498-3

  • Online ISBN: 978-3-319-48499-0

  • eBook Packages: EngineeringEngineering (R0)

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