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This document discusses using machine learning to automatically generate an objective function for a variable neighborhood search algorithm to generate first species counterpoint fragments. It trains a first-order Markov model on a corpus of 1000 manually generated first species fragments to learn dyad transitions. The VNS algorithm then generates counterpoint by minimizing the cross-entropy of dyad transitions compared to the trained Markov model. Preliminary results found the algorithm could generate good quality counterpoint quickly. Future work will apply these methods to higher species counterpoint and train on real musical corpora.
Detailed Information
| Author: | ['Dorien Herremans'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 1 |
| Language: | English |
| Format: | |
| Price: | FREE |
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