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bmusegan

Code for “Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation”

https://github.com/salu133445/bmusegan

Science Score: 64.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    Found .zenodo.json file
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    Academic publication links
    Links to: arxiv.org
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    2 of 3 committers (66.7%) from academic institutions
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    Low similarity (11.6%) to scientific vocabulary

Keywords

binary-neuron generative-adversarial-network machine-learning multi-track music-generation piano-roll

Keywords from Contributors

midi music music-information-retrieval visulization gan
Last synced: 6 months ago · JSON representation ·

Repository

Code for “Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation”

Basic Info
Statistics
  • Stars: 59
  • Watchers: 5
  • Forks: 13
  • Open Issues: 4
  • Releases: 0
Topics
binary-neuron generative-adversarial-network machine-learning multi-track music-generation piano-roll
Created almost 8 years ago · Last pushed over 3 years ago
Metadata Files
Readme Funding License Citation

README.md

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 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).

Run

sh 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 in config.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.
[homepage] [video] [paper] [slides] [slides (long)] [poster] [arXiv] [code]

Owner

  • Name: Hao-Wen (Herman) Dong 董皓文
  • Login: salu133445
  • Kind: user
  • Location: USA/Taiwan
  • Company: UC San Diego

Assistant Professor at University of Michigan | PhD from UC San Diego | Human-Centered Generative AI for Content Generation

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite it using these metadata.
authors:
  - family-names: Dong
    given-names: Hao-Wen
title: BinaryMuseGAN
preferred-citation:
  type: article
  authors:
    - family-names: Dong
      given-names: Hao-Wen
    - family-names: Yang
      given-names: Yi-Hsuan
  title: "Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation"
  journal: Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR)
  year: 2018
date-released: 2018-04-18
license: MIT
url: "https://salu133445.github.io/bmusegan/"
repository-code: "https://github.com/salu133445/bmusegan"

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salu133445 s****5@g****m 97
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citi.sinica.edu.tw: 1 ntu.edu.tw: 1

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