Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina
- Published
- Accepted
- Subject Areas
- Artificial Intelligence, Data Mining and Machine Learning, Multimedia
- Keywords
- genetic programming, clustering, algorithmic composition, dissonance detection, computer music
- Copyright
- © 2019 Anders et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina. PeerJ Preprints 7:e27731v1 https://doi.org/10.7287/peerj.preprints.27731v1
Abstract
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. As negative examples we us all other dissonances; melodic fragments without dissonances; purely random melodic fragments; and slight random transformations of positive examples. Learnt rules circumstantiate melodic features of the dissonance categories very well, though some resulting best rules allow for minor deviations compared with positive examples (e.g., allowing the dissonance category suspension to occur also on shorter notes).
Author Comment
This is a submission to PeerJ Computer Science for review.