Author

B. Hayes

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Preprint Sep 2026

Off-manifold robustness in synthesizer inversion with joint distribution flow matching

Recent work on synthesizer inversion shows that generative models outperform deterministic approaches by explicitly modeling the ambiguity in mapping audio to parameters. Training such models, however, requires audio-parameter pairs, which are typically obtained by rendering sampled or preset parameters through the syn...

B. Hayes · 0 citations

#machine learning Preprint Sep 2026

Synth-JEPA: Joint Embedding Prediction for Renderer-Free Synthesizer Parameter Search

Sound matching can be formulated as optimizing synthesizer parameters against an audio-domain objective. However, objectives derived from generic audio representations are often difficult to optimize, while direct search requires rendering every candidate. We introduce Synth-JEPA, which learns mutually predictive audio...

B. Hayes, Hao-Kun Tian, Stefan Lattner · 0 citations