Style-based Drum Synthesis with GAN Inversion Demo

TensorFlow implementation of a style-based version of the adversarial drum synth (ADS) from the paper Adversarial Synthesis of Drum Sounds @ The 2020 DAFx Conference.

Code

Dependencies

Python

Code has been developed with Python 3.6.13. It should work with other versions of Python 3, but has not been tested. Moreover, we rely on several third-party libraries, listed in requirements.txt. They can be installed with

$ pip install -r requirements.txt

Checkpoints

The tensorflow checkpoints for loading pre-trained network weights can be download here. Unzip the folder and save it into this projects directory: "style-drumsynth/checkpoints".

Usage

The code is contained within the ads_demo.py script, which enables conditional synthesises of drum sounds using a pretrained generator.

The following control parameters are available:

• Condition: which type of drum to generation (kick, snare or hat)
• Direction: "features", which principal direction to move in
• Direction slider: How far to move in a particular direction
• Number of generations: How many drums to generate
• Stocastic Variation: Amount of inconsequential noise to inject into the generator
• Randomize: Generate by randomly sampling the latent space, or generate from a fixed, pre-computed latent vectors for a kick, snare and hat
• Encode: regenerate drum sounds stored in the ads_demo/input_audio

Generations are saved in the ads_demo/generations folder. Pretrained model weights are saved in the ads_demo/checkpoints folder.

train.py arguments

  -c CONDITION,           --condition CONDITION
                            0: kick, 1: snare, 2:hat
  -d DIRECTION,           --direction DIRECTION
                            synthesis controls [0:4]
  -ds DIRECTION_SLIDER,   --direction_slider DIRECTION_SLIDER
                            how much to move in a particular direction
  -n NUM_GENERATIONS,     --num_generations NUM_GENERATIONS
                            number of examples to generate
  -v STOCASTIC_VARIATION, --stocastic_variation STOCASTIC_VARIATION
                            amount of inconsequential noise injected
  -r RANDOMIZE,           --randomize RANDOMIZE
                            if set to False, a fixed latent vector is used to generate a drum sound from each condition
  -e ENCODE,              --encode ENCODE
                            regenerates drum sounds from encoder folder

Supporting webpage

For more information, please visit the corresponding supporting website.

It contains the following:

• Audio examples
• Training data
• Generations
• Example usage within loop-based electronic music compositions
• Generating Drum Loops
• Interpolation demonstration
• Supplementary figures
• A link to the DAFx 2020 paper and presentation

References

[1]	Drysdale, J., M. Tomczak, J. Hockman, Adversarial Synthesis of Drum Sounds. Proceedings of the 23rd International Conference on Digital Audio Effects (DAFX), 2020.

@inproceedings{drysdale2020ads,
  title={Adversarial synthesis of drum sounds},
  author={Drysdale, Jake and Tomczak, Maciek and Hockman, Jason},
  booktitle = {Proceedings of the International Conference on Digital Audio Effects (DAFx)},
  year={2020}
}

Help

Any questions please feel free to contact me on jake.drysdale@bcu.ac.uk