ToneTwist AFx Dataset: Amplitube DComp

Settings Output Sensitivity -14dB 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}, }数据集配置 输出参数：灵敏度为-14dB，参数取值为10 带同步标记的干声素材 干声输入为多来源的纯净吉他与贝斯录音合集，具体如下： - IDMT-SMT-GUITAR - 数据集2，时长7分23秒 - IDMT-SMT-GUITAR - 数据集4 - Career SG，时长6分08秒 - IDMT-SMT-GUITAR - 数据集4 - Ibanez 2820，时长5分14秒 - IDMT-SMT-Bass-Single-Track，时长5分58秒 - 神经放大器建模器（Neural Amp Modeler，以下简称NAM），时长3分11秒 - 私人吉他录音数据集，时长5分19秒 - YouTube平台贝斯录音合集，时长10分09秒 预处理流程 通用预处理规则：所有音频文件的首尾均添加2个同步脉冲标记 针对IDMT-SMT-GUITAR - 数据集2：将峰值归一化至满刻度分贝（dBFS）-6dB 针对NAM：无预处理操作 针对其余素材：将峰值归一化至-0.1dBFS，并每5秒乘以一个服从均匀分布[0.1, 1.0]（对应增益范围[-20dB, 0dB]）的随机数 作者信息 Marco Comunità — 伦敦玛丽女王大学数字音乐中心 开源仓库：https://github.com/mcomunita/tonetwist-afx-dataset 引用说明 若您使用本音频效果数据集，请引用以下文献： @misc{comunità2025nablafxframeworkdifferentiableblackbox, title={NablAFx：可微分黑盒与灰盒音频效果建模框架}, 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}, }