cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10892011
title: ToneTwist AFx Dataset - External: Marshall JVM410H - Channel: OD1

description: Settings


Recorded both at preamp out and speaker out.

 




Bass
Middle
Treble
Gain


0
0
0
5


0
0
10
5


0
5
5
5


0
10
0
5


0
10
10
5


1
3
7
5


2
5
5
5




3

7
10
5




4

5
5
5




5

0
5
5




5

2
5
5




5

4
5
5




5

5
0
5




5

5
2
5




5

5
4
5




5

5
5
0




5

5
5
1




5

5
5
2




5

5
5
4




5

5
5
5




5

5
5
6




5

5
5
8




5

5
5
10




5

5
6
5




5

5
8
5




5

5
10
5




5

6
5
5




5

8
5
5




5

10
5
5




6

5
5
5




6.5

8.5
3.5
5


8
5
5
5




10

0
0
5




10

0
10
5




10

5
5
5




10

10
0
5




10

10
10
5




Github

https://github.com/mcomunita/tonetwist-afx-dataset



Source

Data used in the following publication:


@inproceedings{miklanek2023neural,
title={Neural grey-box guitar amplifier modelling with limited data},
author={Miklanek, Stepan and Wright, Alec and V{\"a}lim{\"a}ki, Vesa and Schimmel, Jiri},
booktitle={International Conference on Digital Audio Effects},
pages={151--158},
year={2023},
organization={Aalborg University}



Web


https://stepanmk.github.io/grey-box-amp

Code

https://github.com/stepanmk/grey-box-amp

Original Data

https://zenodo.org/records/7970723
DOI: 10.5281/zenodo.10892012
title: ToneTwist AFx Dataset - External: Marshall JVM410H - Channel: OD1

description: Settings


Recorded both at preamp out and speaker out.

 




Bass
Middle
Treble
Gain


0
0
0
5


0
0
10
5


0
5
5
5


0
10
0
5


0
10
10
5


1
3
7
5


2
5
5
5




3

7
10
5




4

5
5
5




5

0
5
5




5

2
5
5




5

4
5
5




5

5
0
5




5

5
2
5




5

5
4
5




5

5
5
0




5

5
5
1




5

5
5
2




5

5
5
4




5

5
5
5




5

5
5
6




5

5
5
8




5

5
5
10




5

5
6
5




5

5
8
5




5

5
10
5




5

6
5
5




5

8
5
5




5

10
5
5




6

5
5
5




6.5

8.5
3.5
5


8
5
5
5




10

0
0
5




10

0
10
5




10

5
5
5




10

10
0
5




10

10
10
5




Github

https://github.com/mcomunita/tonetwist-afx-dataset



Source

Data used in the following publication:


@inproceedings{miklanek2023neural,
title={Neural grey-box guitar amplifier modelling with limited data},
author={Miklanek, Stepan and Wright, Alec and V{\"a}lim{\"a}ki, Vesa and Schimmel, Jiri},
booktitle={International Conference on Digital Audio Effects},
pages={151--158},
year={2023},
organization={Aalborg University}



Web


https://stepanmk.github.io/grey-box-amp

Code

https://github.com/stepanmk/grey-box-amp

Original Data

https://zenodo.org/records/7970723
DOI: 10.5281/zenodo.10796415
title: ToneTwist AFx Dataset: Mooer Trelicopter

description: Settings




Depth
Bias
Speed


8
1
5


8
5
1


8
5
5


8
5
10


8
10
5




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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10901455
title: ToneTwist AFx Dataset: Multidrive Pedal Pro M-Distortion

description: Settings




Gain
Level
Bass
Treble
Mid Level
Mid Frequency


0
5
10
10
10
5


1
5
10
10
10
5


3
5
10
10
10
5


5
5
10
10
10
5


7
5
10
10
10
5


9
5
10
10
10
5


10
5
0
10
10
5


10
5
1
10
10
5


10
5
3
10
10
5


10
5
5
10
10
5


10
5
7
10
10
5


10
5
9
10
10
5


10
5
10
0
10
5


10
5
10
1
10
5


10
5
10
3
10
5


10
5
10
5
10
5


10
5
10
7
10
5


10
5
10
9
10
5


10
5
10
10
0
5


10
5
10
10
1
5


10
5
10
10
3
5


10
5
10
10
5
5


10
5
10
10
7
5


10
5
10
10
9
5


10
5
10
10
10
0


10
5
10
10
10
1


10
5
10
10
10
3


10
5
10
10
10
5


10
5
10
10
10
7


10
5
10
10
10
9


10
5
10
10
10
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:

@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}, 
}
DOI: 10.5281/zenodo.10796416
title: ToneTwist AFx Dataset: Mooer Trelicopter

description: Settings




Depth
Bias
Speed


8
1
5


8
5
1


8
5
5


8
5
10


8
10
5




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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10901454
title: ToneTwist AFx Dataset: Multidrive Pedal Pro M-Distortion

description: Settings




Gain
Level
Bass
Treble
Mid Level
Mid Frequency


0
5
10
10
10
5


1
5
10
10
10
5


3
5
10
10
10
5


5
5
10
10
10
5


7
5
10
10
10
5


9
5
10
10
10
5


10
5
0
10
10
5


10
5
1
10
10
5


10
5
3
10
10
5


10
5
5
10
10
5


10
5
7
10
10
5


10
5
9
10
10
5


10
5
10
0
10
5


10
5
10
1
10
5


10
5
10
3
10
5


10
5
10
5
10
5


10
5
10
7
10
5


10
5
10
9
10
5


10
5
10
10
0
5


10
5
10
10
1
5


10
5
10
10
3
5


10
5
10
10
5
5


10
5
10
10
7
5


10
5
10
10
9
5


10
5
10
10
10
0


10
5
10
10
10
1


10
5
10
10
10
3


10
5
10
10
10
5


10
5
10
10
10
7


10
5
10
10
10
9


10
5
10
10
10
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:

@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}, 
}
DOI: 10.5281/zenodo.10796359
title: ToneTwist AFx Dataset: Harley Benton Plexicon

description: Settings




Volume
Tone
Gain
Mode


10
1
5
Normal


10
5
1
Normal


10
5
5
Bright


10
5
5
Normal


10
5
10
Normal


10
10
5
Normal


10
10
10
Bright




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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10796358
title: ToneTwist AFx Dataset: Harley Benton Plexicon

description: Settings




Volume
Tone
Gain
Mode


10
1
5
Normal


10
5
1
Normal


10
5
5
Bright


10
5
5
Normal


10
5
10
Normal


10
10
5
Normal


10
10
10
Bright




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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10794775
title: ToneTwist AFx Dataset: Harley Benton Drop Kick

description: Settings




Volume
Tone
Gain
Mode


10
1
5
Normal


10
5
1
Normal


10
5
5
Normal


10
5
5
Soft


10
5
5
Tense


10
5
10
Normal


10
10
5
Normal




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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10794776
title: ToneTwist AFx Dataset: Harley Benton Drop Kick

description: Settings




Volume
Tone
Gain
Mode


10
1
5
Normal


10
5
1
Normal


10
5
5
Normal


10
5
5
Soft


10
5
5
Tense


10
5
10
Normal


10
10
5
Normal




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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10901450
title: ToneTwist AFx Dataset: Multidrive Pedal Pro F-Fuzz

description: Settings




Fuzz
Volume


0
8


1
8


2
8


3
8


4
8


5
8


6
8


7
8


8
8


9
8


10
8




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:

@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}, 
}
DOI: 10.5281/zenodo.10901449
title: ToneTwist AFx Dataset: Multidrive Pedal Pro F-Fuzz

description: Settings




Fuzz
Volume


0
8


1
8


2
8


3
8


4
8


5
8


6
8


7
8


8
8


9
8


10
8




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:

@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}, 
}
DOI: 10.5281/zenodo.10794614
title: ToneTwist AFx Dataset: Fulltone Full Drive 2

description: Settings




Volume
Tone
Drive
Boost


10
5
1
0


10
5
5
0


10
5
10
0


10
10
10
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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10794615
title: ToneTwist AFx Dataset: Fulltone Full Drive 2

description: Settings




Volume
Tone
Drive
Boost


10
5
1
0


10
5
5
0


10
5
10
0


10
10
10
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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}
DOI: 10.5281/zenodo.10796332
title: ToneTwist AFx Dataset: Harley Benton Green Tint

description: Settings




Volume
Tone
Gain


10
1
10


10
5
1


10
5
5


10
5
10


10
10
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 AUDIO-EFFECTS-DATASET, please cite the following publication:

@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}, 
}