BinaryMuseGAN is a follow-up project of the MuseGAN project. In this project, we first investigate how the real-valued piano-rolls generated by the generator may lead to difficulties in training the discriminator for CNN-based models. To overcome the binarization issue, we propose to append to the generator an additional refiner network, which try to refine the real-valued predictions generated by the pretrained generator to binary-valued ones. The proposed model is able to directly generate binary-valued piano-rolls at test time.
We trained the network with training data collected from Lakh Pianoroll Dataset. We used the model to generate four-bar musical phrases consisting of eight tracks: Drums, Piano, Guitar, Bass, Ensemble, Reed, Synth Lead and Synth Pad. Audio samples are available here.
Modify config.py for configuration.
Quick setup
Change the values in the dictionary SETUP for a quick setup. Documentation
is provided right after each key.
More configuration options
Four dictionaries EXP_CONFIG, DATA_CONFIG, MODEL_CONFIG and
TRAIN_CONFIG define experiment-, data-, model- and training-related
configuration variables, respectively.
The automatically-determined experiment name is based only on the values defined in the dictionary
SETUP, so remember to provide the experiment name manually (so that you won't overwrite a trained model).
python main.pyPrepare your own data
The array will be reshaped to (-1, num_bar, num_timestep, num_pitch,
num_track). These variables are defined in config.py.
Download our training data with this script or download it manually here.
Convolutional Generative Adversarial Networks with Binary Neurons for
Polyphonic Music Generation
Hao-Wen Dong and Yi-Hsuan Yang
in Proceedings of the 19th International Society for Music Information
Retrieval Conference (ISMIR), 2018.
[website]
[arxiv]
[paper]
[slides(long)]
[slides(short)]
[poster]
[code]