A peer-reviewed version of this preprint was published in PeerJ on 16 December 2019. View the peer-reviewed version (peerj.com/articles/cs-244), which is the preferred citable publication unless you specifically need to cite this preprint. Anders T, Inden B. 2019. Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina. PeerJ Computer Science 5:e244 https://doi.org/10.7717/peerj-cs.244 Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina Torsten Anders 1 , Benjamin Inden Corresp. 2 1 School of Media Arts and Performance, University of Bedfordshire, Luton, Bedfordshire, United Kingdom 2 Department of Computer Science and Technology, Nottingham Trent University Corresponding Author: Benjamin Inden Email address:
[email protected] We describe a method to automatically extract symbolic compositional rules from music corpora that can be combined with each other and manually programmed rules for algorithmic composition, and some preliminary results of applying that method. As machine learning technique we chose genetic programming, because it is capable of learning formula consisting of both logic and numeric relations. Genetic programming was never used for this purpose to our knowledge. We therefore investigate a well understood case in this pilot study: the dissonance treatment in Palestrina’s music. We label dissonances with a custom algorithm, automatically cluster melodic fragments with labelled dissonances into different dissonance categories (passing tone, suspension etc.) with the DBSCAN algorithm, and then learn rules describing the dissonance treatment of each category with genetic programming. As positive examples we use dissonances from a given category.