Simulation-Based Inference for Plate Reverb System Identification

Abstract:We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse response. To do so, we use the Simulation-Based Inference (SBI) framework, in which we train a neural network to estimate a density over plate parameters given an impulse response, using a dataset generated by the simulator. Inference for a new impulse response then requires only a forward pass through the network, without involving the simulator. For each test observation, we fine-tune a specific network: additional simulation rounds are performed by sampling parameters from the current estimated distribution, simulating the corresponding impulse responses, and fine-tuning to produce the specialized network.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)

arXiv:2609.35295 [eess.AS]

(or arXiv:2609.35295v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.35295 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proc. 29th Int. Conf. Digital Audio Effects (DAFx26), Cambridge, MA, USA, 1-4 Sept. 2026 (Parameter Estimation Challenge, Task A)

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