  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.
