Inferring pre-defined Hierarchical Wave Function Collapse constraints from MIDI files

Bachelor thesis (2024)

Authors

C. van den Oudenhoven Electrical Engineering, Mathematics and Computer Science

Contributors

Rafael Bidarra Computer Graphics and Visualisation - EEMCS (mentor)
Joana Gonçalves Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Published Date

28-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

AI-generated music is a huge research field with many different approaches and models being the result of it. One such model is the ProceduraLiszt model, which utilizes the Wave Function Collapse algorithm, an algorithm similar to constraint programming, to generate its music. This research builds upon that model. It does so by trying to reverse engineer a given piece of music into a set of satisfied constraints that the model is compatible with. We present an approach that allows for the inference of constraints of a given music file that adheres to the MIDI format called MidiAnalyser. We run our model on a set of music files and analyze the inferred constraints. The constraints include aspects like key and note range.

Files

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