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A Bayesian Approach to Classify the Music Scores on the Basis of the Music Style

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Intelligent Decision Technologies 2016

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 57))

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Abstract

This article presents a new version of the algorithm proposed by Della Ventura (12th TELE-INFO International Conference on Recent Researches in Telecommunications, and Informatics, 2013, [1]) to classify the musical scores. Score classification means an automatic process of assignment of the specific score to a certain class or category: baroque, romantic or contemporary music. The algorithm is based on a Bayesian probabilistic model that extends the Naive Bayes classifier by adding a variable tied to the value of the information contained within the. The score is not seen as a single entity, but as a set of subtopics, every single one of which identifies and represents a standard feature of music writing. The classification of the score is done on the basis of its subtopics: an intermediate level of classification is thus introduced, which induces a hierarchical classification. The new algorithm performs equally well on the old dataset, but gives much better results on the new larger and more diverse dataset.

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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. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, UK

    Robert J. Howlett

  4. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Ventura, M.D. (2016). A Bayesian Approach to Classify the Music Scores on the Basis of the Music Style. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-39627-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39626-2

  • Online ISBN: 978-3-319-39627-9

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