ToneTwist AFx Dataset: Harley Benton Big Fur

Settings

Volume
Tone
Sustain

10
1
5

10
5
1

10
5
5

10
5
10

10
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},
}

参数设置

音量（Volume）、音色（Tone）与延音（Sustain）

10 1 5
10 5 1
10 5 5
10 5 10
10 10 5

带标记的干声信号

干声输入为来自多源的纯净吉他与贝斯录音，具体如下：

1. IDMT-SMT-GUITAR - 数据集2（时长7分23秒）
2. IDMT-SMT-GUITAR - 数据集4 - Career SG（时长6分08秒）
3. IDMT-SMT-GUITAR - 数据集4 - Ibanez 2820（时长5分14秒）
4. IDMT-SMT-Bass-Single-Track（时长5分58秒）
5. NAM（神经放大器建模器，Neural Amp Modeler）（时长3分11秒）
6. 私人吉他录音数据（时长5分19秒）
7. YouTube平台贝斯录音（时长10分09秒）

预处理流程

通用预处理：
为每个音频文件的首尾添加同步标记（2个脉冲信号）

针对IDMT-SMT-GUITAR - 数据集2：
峰值归一化至-6dBFS

针对NAM：
无预处理流程

其余数据集：
峰值归一化至-0.1dBFS；每5秒将信号乘以服从均匀分布区间[0.1, 1.0]（对应增益范围为-20dB至0dB）的随机数

作者

马可·科穆尼塔（Marco Comunità）—— 伦敦大学玛丽女王学院数字音乐中心

GitHub仓库地址：https://github.com/mcomunita/tonetwist-afx-dataset

引用说明

若您使用本AUDIO-EFFECTS-DATASET，请引用以下学术文献：

@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},
}