One Billion Audio Sounds from GPU-enabled Modular Synthesis

• URL: http://arxiv.org/abs/2104.12922v1
• Date: Tue, 27 Apr 2021 00:38:52 GMT
• Title: One Billion Audio Sounds from GPU-enabled Modular Synthesis
• Authors: Joseph Turian and Jordie Shier and George Tzanetakis and Kirk McNally and Max Henry
• Abstract summary: synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, is 100x larger than any audio dataset in the literature. synth1B1 samples are deterministically generated on-the-fly 16200x faster than real-time (714MHz) on a single GPU.
• Score: 5.5022962399775945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We release synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, which is 100x larger than any audio dataset in the literature. Each sound is paired with the corresponding latent parameters used to generate it. synth1B1 samples are deterministically generated on-the-fly 16200x faster than real-time (714MHz) on a single GPU using torchsynth (https://github.com/torchsynth/torchsynth), an open-source modular synthesizer we release. Additionally, we release two new audio datasets: FM synth timbre (https://zenodo.org/record/4677102) and subtractive synth pitch (https://zenodo.org/record/4677097). Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations. Finally, we propose novel approaches to synthesizer hyperparameter optimization, and demonstrate how perceptually-correlated auditory distances could enable new applications in synthesizer design.

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