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.
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.
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