3.4 hours of audio synthesized using the open-source Surge synthesizer, based upon 2084 presets included in the Surge package. These represent ``natural'' synthesis sounds---i.e.presets devised by humans. We generated 4-second samples playing at velocity 64 with a note-on duration of 3 seconds. For each preset, we varied only the pitch, from MIDI 21--108, the range of a grand piano. Every sound in the dataset was RMS-level normalized using the normalize package. There was no elegant way to dedup this dataset; however only a small percentage of presets (like drums and sound effects) had no perceptual pitch variation or ordering. We used the Surge Python API to generate this dataset. Applications of this dataset include: Pitch prediction Pitch ranking within a preset Predict a sound's preset If you use this dataset in published researched, please cite Turian et al., "One Billion Audio Sounds from GPU-enabled Modular Synthesis", in Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx2020), 2021: @inproceedings{turian2021torchsynth, title = {One Billion Audio Sounds from {GPU}-enabled Modular Synthesis}, author = {Joseph Turian and Jordie Shier and George Tzanetakis and Kirk McNally and Max Henry}, year = 2021, month = Sep, booktitle = {Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx2020)}, location = {Vienna, Austria} }

{"references": ["Turian et. al (2021). One Billion Audio Sounds from GPU-enabled Modular Synthesis. arXiv:2104.12922"]}