🔊 Follow the links above for audio examples, full training code, and the arXiv pre-print.

🏆 We then apply this method to a dataset of sounds sampled from Surge XT — a feature rich software synthesizer — and find that it dramatically outperforms state-of-the-art baselines on audio reconstruction.

🤔 However, in the case of real synthesizers, we may not know the appropriate symmetries a priori. To allow them to be discovered adaptively, we introduce a technique called Param2Tok, which learns a mapping from synthesizer parameters to model tokens.

📈 We design a toy task that isolates this phenomenon and find that the presence of permutation symmetry degrades the performance of conventional methods. We then show that a generative approach, which can assign predictive weight to multiple possible solutions, performs considerably better.

Very excited to share that our latest work, "Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching", has been accepted to ISMIR 2025 in Daejon, Korea!

Paper: arxiv.org/abs/2506.07199
Audio: benhayes.net/synth-perm/
Code: github.com/ben-hayes/sy...

🧵