Awesome
BinaryMuseGAN
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.
Run the code
Configuration
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
andTRAIN_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).
Run
python main.py
Training data
-
Prepare your own data
The array will be reshaped to (-1,
num_bar
,num_timestep
,num_pitch
,num_track
). These variables are defined inconfig.py
. -
Download our training data with this script or download it manually here.
Citing
Please cite the following paper if you use the code provided in this repository.
Hao-Wen Dong and Yi-Hsuan Yang, "Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation," Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018.<br> [homepage] [video] [paper] [slides] [slides (long)] [poster] [arXiv] [code]