MIDI-VAE

Paper

MIDI-VAE: MODELING DYNAMICS AND INSTRUMENTATION OF MUSIC WITH APPLICATIONS TO STYLE TRANSFER

Paper accepted at 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, September 2018

Music Samples

www.youtube.com/channel/UCCkFzSvCae8ySmKCCWM5Mpg

Dataset

All the music pieces we used for generating the audio samples on Youtube and the evaluation in the paper can be downloaded here: https://goo.gl/sNpgQ7

Preparation

Install common libraries like numpy matplotlib pickle numpy progressbar sklearn scipy csv keras tensorflow theano (some functions are only supported with theano because of recurrentshop)
Make sure you have installed the following packages https://github.com/craffel/pretty-midi https://github.com/farizrahman4u/recurrentshop/tree/master/recurrentshop https://github.com/nschloe/matplotlib2tikz
Put your midi data in the folder 'data/original/'
Group them into folders and name than for example 'style1', 'style2'
Make sure you have at least 10 midi files per style, otherwise it can't form a test set
Insert your style names into classes variable in settings.py
Adjust parameters for training in settings.py
Make sure you have all these files in the same folder

Training

Run either vae_training.py to use the full MIDI-VAE model or
Run any of the style classifiers pitch_classifier.py, velocity_classifer.py or instrument_classifer.py

The models will be stored in the automatically generated folder models/

Evaluation

Change the model_name and epoch of your MIDI-VAE model that you want to evaluate
Change the model names and epochs and weights for all the style classifiers
Make sure you have set the same parameters as were used during training
Run vae_evaluation.py