ToneTwist AFx Dataset: Custom Dynamic Fuzz

Settings Gain Sensitivity Attack Release Volume 5 10 1ms 2500ms 10 Dry with markers Dry inputs are a selection of clean guitar and bass recordings from different sources: IDMT-SMT-GUITAR - dataset 2 (7:23 min) IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min) IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min) IDMT-SMT-Bass-Single-Track - (5:58 min) NAM: Neural Amp Modeler - (3:11 min) Private Guitar Data - (5:19 min) YouTube Bass Recordings - (10:09 min) Pre-processing: All: synchronization markers (2 impulses) added at start and end of every file IDMT-SMT-GUITAR - dataset 2: peak normalized to -6dBFS NAM: no pre-processing Others: peak normalized to -0.1dBFS signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB]) Authors Marco Comunità - Centre for Digital Music, Queen Mary University of London Github https://github.com/mcomunita/tonetwist-afx-dataset Reference If you make use of this dataset, please cite the following publications: @inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} } @misc{comunità2025nablafxframeworkdifferentiableblackbox, title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss}, year={2025}, eprint={2502.11668}, archivePrefix={arXiv}, primaryClass={cs.SD}, url={https://arxiv.org/abs/2502.11668}, }