Code needed for building the data and learner for predicting chiptunes using MIDI representations
data/
raw/*
- All of the MIDI files that have been generated and collected so
far
all_songs/*
- A subset of the *raw* directory that only includes songs that
have been manually labeled as "songs", as opposed to empty
tracks, sound effects or other background songs.
training_songs/*
- A subset of the *all_songs* directory, approx 2/3 of it,
containing songs that should be used to generate and train
the algorithm with.
testing_songs/*
- A subset of the *all_songs* directory, containing the approx
1/3 of the songs that are not in the *training_songs* directory,
that should be used for testing the accuracy of the algorithm
training_counts.sqlite3
- The generated sqlite3 database of transition counts (generated
with the *training.py* file) based off the *training_songs*
directory
install python-midi by running:
cd contrib/python-midi
python setup.py install
install MIDIUtil by running:
cd contrib/MIDIUtil
python setup.py install
You'll also want to generate some training counts. You can do so with:
python training.py
- nes2midi Windows tool for converting NSF files to MIDI. The original software is in Japanese, but there is thankfully an English translation too!
- WINE Windows DLL implementation used for running the above software on OSX and Linux. OSX instructions are available on the WINE site.