Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina
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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).
Cite this as
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 PeerJ Preprints 7:e27731v1 https://doi.org/10.7287/peerj.preprints.27731v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ Computer Science for review.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Torsten Anders conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Benjamin Inden conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
DOI: 10.5281/zenodo.2653503
Funding
The authors received no funding for this work.
