Differentiable All-pole Filters for Time-varying Audio Systems

Chin-Yun Yu, Christopher Mitcheltree, Alistair Carson, Stefan Bilbao, Joshua D. Reiss, and György Fazekas

Time-varying Subtractive Synthesizer (Roland TB-303 Bass Line) Experiments

Instructions for Reproducibility

1. Clone this repository and open its directory.
2. Initialize and update the submodules (git submodule update --init --recursive).
3. Install the requirements using
   conda env create --file=conda_env_cpu.yml or
   conda env create --file=conda_env.yml
   for GPU acceleration.
   requirements_pipchill.txt and requirements_all.txt are also provided as references, but are not needed when using the conda_env.yml files.
4. The source code can be explored in the acid_ddsp/ directory.
5. All models from the paper can be found in the models/ directory.
6. All eval results from the paper can be found in the eval/ directory.
7. All Neutone files for running the models and the acid synth implementations as a VST in a DAW can be found in the neutone/ directory.
8. Create an out directory (mkdir out).
9. All models can be evaluated by modifying and running scripts/test.py.
   Make sure your PYTHONPATH has been set correctly by running a command like
   export PYTHONPATH=$PYTHONPATH:BASE_DIR/acid_ddsp/,
   export PYTHONPATH=$PYTHONPATH:BASE_DIR/torchlpc/, and
   export PYTHONPATH=$PYTHONPATH:BASE_DIR/fadtk/.
10. CPU benchmark values can be obtained by running scripts/benchmark.py.
    These will vary depending on your computer.
11. (Optional) All models can be trained by modifying configs/abstract_303/train.yml and running scripts/train.py.
    Before training, scripts/preprocess_data.py should be run to create the dataset.
12. (Optional) Custom Neutone models can be exported by modifying and running scripts/export_neutone_models.py or scripts/export_neutone_synth.py.
13. The source code is currently not documented, but don't hesitate to open an issue if you have any questions or comments.