Install the requirements.
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -r requirements.txt
You can find our dataset of fuzz and compressor effects on Zenodo. Once downloaded you can replace the data folder in this repo. You can also use your data which goes into 3 subfolders: test, train and val. Each folder should contain the input and target files (wav - 16bit) (one file only - it will be split into samples during training) and they should be named DEVICE-input.wav and DEVICE-target.wav (e.g., la2a-input.wav and la2a-target.wav).
If would like to re-train the models in the paper, you can run the training script which will train all the models one by one. The training results will be saved in the results folder.
python train.py
To process audio using a trained model you can run the proc_audio.py script.
python proc_audio.py \
--input_file data/test/facebender-input.wav \
--output_file facebender-output.wav \
--model_file results/MODEL_FOLDER/model_best.json
--chunk_length 32768
https://github.com/Alec-Wright/Automated-GuitarAmpModelling
https://github.com/csteinmetz1/micro-tcn
If you use any of this code in your work, please consider citing us.
@misc{https://doi.org/10.48550/arxiv.2211.00497,
doi = {10.48550/ARXIV.2211.00497},
url = {https://arxiv.org/abs/2211.00497},
author = {Comunità, Marco and Steinmetz, Christian J. and Phan, Huy and Reiss, Joshua D.},
keywords = {Sound (cs.SD), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {Modelling black-box audio effects with time-varying feature modulation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}