cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10796333
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}, 
}
DOI: 10.5281/zenodo.10796377
title: ToneTwist AFx Dataset: Harley Benton Rodent

description: Settings




Volume
Filter
Distortion
Mode


10
1
5
Normal


10
5
3
Normal


10
5
5
Normal


10
5
5
Solo


10
5
5
Turbo


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.10796378
title: ToneTwist AFx Dataset: Harley Benton Rodent

description: Settings




Volume
Filter
Distortion
Mode


10
1
5
Normal


10
5
3
Normal


10
5
5
Normal


10
5
5
Solo


10
5
5
Turbo


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.10796425
title: ToneTwist AFx Dataset: Yuer Dyna Compressor

description: Settings




Attack
Sustain
Level


1
1
10


1
5
10


1
10
10


5
5
10


5
10
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.10796426
title: ToneTwist AFx Dataset: Yuer Dyna Compressor

description: Settings




Attack
Sustain
Level


1
1
10


1
5
10


1
10
10


5
5
10


5
10
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.10901400
title: ToneTwist AFx Dataset: Multidrive Pedal Pro 808-Scream

description: Settings




Overdrive
Level
Tone


0
8
10


1
8
10


2
8
10


3
8
10


4
8
10


5
8
10


6
8
10


7
8
10


8
8
10


9
8
10


10
8
0


10
8
1


10
8
2


10
8
3


10
8
4


10
8
5


10
8
6


10
8
7


10
8
8


10
8
9


10
8
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.10901401
title: ToneTwist AFx Dataset: Multidrive Pedal Pro 808-Scream

description: Settings




Overdrive
Level
Tone


0
8
10


1
8
10


2
8
10


3
8
10


4
8
10


5
8
10


6
8
10


7
8
10


8
8
10


9
8
10


10
8
0


10
8
1


10
8
2


10
8
3


10
8
4


10
8
5


10
8
6


10
8
7


10
8
8


10
8
9


10
8
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: 10.15468/dl.chu69p
title: Occurrence Download

description: A dataset containing 896 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Salmo salar Linnaeus, 1758"
 ]
}

 The dataset includes 896 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004394-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10901416
title: ToneTwist AFx Dataset: Multidrive Pedal Pro B-Drive

description: Settings




Gain
Level
Tone


0
8
10


1
8
10


2
8
10


3
8
10


4
8
10


5
8
10


6
8
10


7
8
10


8
8
10


9
8
10


10
8
0


10
8
1


10
8
2


10
8
3


10
8
4


10
8
5


10
8
6


10
8
7


10
8
8


10
8
9


10
8
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.10901417
title: ToneTwist AFx Dataset: Multidrive Pedal Pro B-Drive

description: Settings




Gain
Level
Tone


0
8
10


1
8
10


2
8
10


3
8
10


4
8
10


5
8
10


6
8
10


7
8
10


8
8
10


9
8
10


10
8
0


10
8
1


10
8
2


10
8
3


10
8
4


10
8
5


10
8
6


10
8
7


10
8
8


10
8
9


10
8
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: 10.15468/dl.6mgeh5
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Human Observation)",
 "Continent is Asia",
 "Country is one of (China, India, Bangladesh, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Terminalia citrina (Gaertn.) Roxb. ex Fleming"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004393-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14883599
title: Dataset to: "Bio-based fertilisers can replace conventional inorganic P fertilisers under European pedoclimatic conditions"

description: Harvest data of five field trials across Europe for the assessment of the P fertiliser value of eight bio-based fertilisers.
DOI: 10.5281/zenodo.14883600
title: Dataset to: "Bio-based fertilisers can replace conventional inorganic P fertilisers under European pedoclimatic conditions"

description: Harvest data of five field trials across Europe for the assessment of the P fertiliser value of eight bio-based fertilisers.
DOI: 10.5281/zenodo.12772808
title: SHREC

description: SHREC 2021 datast, formated as a tarball to be used as a julia artefact.
DOI: 10.5281/zenodo.12772809
title: SHREC

description: SHREC 2021 datast, formated as a tarball to be used as a julia artefact.
DOI: 10.5281/zenodo.12772963
title: SHREC

description: SHREC 2021 datast, formated as a tarball to be used as a julia artefact.
DOI: 10.5281/zenodo.14886938
title: SHREC

description: SHREC 2021 datast, formated as a tarball to be used as a julia artefact.
DOI: 10.5281/zenodo.10901349
title: ToneTwist AFx Dataset: Arturia Rev Spring 636 - Germanium Preamp/Fuzz

description: Settings




Mode
Input Gain
Blend
Output Gain


Aux
50
0
-24dB




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.10901350
title: ToneTwist AFx Dataset: Arturia Rev Spring 636 - Germanium Preamp/Fuzz

description: Settings




Mode
Input Gain
Blend
Output Gain


Aux
50
0
-24dB




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: 10.15468/dl.9nd4f5
title: Occurrence Download

description: A dataset containing 232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Picea crassifolia Kom."
 ]
}

 The dataset includes 232 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004396-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/10782283
title: World Stock Prices ( Daily Updating )
DOI: 10.5281/zenodo.10901340
title: ToneTwist AFx Dataset: Amplitube Fender Comp

description: Settings




Type


Even Higher




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.10901339
title: ToneTwist AFx Dataset: Amplitube Fender Comp

description: Settings




Type


Even Higher




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}, 
}