ch0p1n is a Python package for music generation at the motivic level.
See My Approach to Automatic Musical Composition for an introduction to the theory and the algorithm behind ch0p1n.
ch0p1n is still under construction.
Python Package for Automatic Musical Composition
Home Page: https://flujoo.github.io/en/my-approach-to-automatic-musical-composition/
ch0p1n is a Python package for music generation at the motivic level.
See My Approach to Automatic Musical Composition for an introduction to the theory and the algorithm behind ch0p1n.
ch0p1n is still under construction.
Hi,
someone on hackernews made the following comment:
Most of this writeup is just a reinvention of Schenkerian analysis, and suffers from the same problem, in that you exercise a lot of editorial judgement in deciding which parts are the core/structural ideas and which parts are embellishment. That undermines the whole idea that this is automatic composition, because you are deciding a heck of a lot upfront. https://en.wikipedia.org/wiki/Schenkerian_analysis
I think you did a great blog and this project is fascinating. However, the graal to me would be to produce a software that can take a music as an input and "easily" allow to generate variations of the composition.
As such I'm wondering wether machine learning techniques, such as a neural network would be useful, not to generate the music but to help your rule based program to generate it by classifying/detecting in a song, which notes are the most likely to be parts of what you call "limited core materials" AKA the primitives to remix.
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