id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_mowcu4jdxvfyvmw4r3nsi27c2e	Torsten Anders	Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina	2019	19	.pdf	application/pdf	9533	958	58	and then learn rules describing the dissonance treatment of each category with GP. Keywords Counterpoint, Rule learning, Palestrina, Genetic programming, Clustering, Algorithmic composition, Dissonance detection, Computer music automatically clustered into different dissonance categories (passing notes, suspensions Note that this algorithm does not implement any knowledge of the dissonance categories To initiate rule learning, our algorithm compiles for each identified cluster (dissonance category) a set of three-note-long learning examples with a dissonance as middle note. be used in the learnt rules: the duration of the dissonant note (durationi), its predecessor For each cluster (dissonance category) at least one learnt rule constrains the treatment Machine learning of symbolic compositional rules with genetic programming: Dissonance Machine learning of symbolic compositional rules with genetic programming: Dissonance Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina	./cache/work_mowcu4jdxvfyvmw4r3nsi27c2e.pdf	./txt/work_mowcu4jdxvfyvmw4r3nsi27c2e.txt