# Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response

arXiv: https://arxiv.org/abs/2608.00656 · HTML: https://mlsanity.com/paper/2608.00656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 1 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [DiffSound: Differentiable Modal Sound Rendering and Inverse Rendering for Diverse Inference Tasks](https://mlsanity.com/paper/2409.13486/) (2024)
- [Learning to Solve Inverse Problems for Perceptual Sound Matching](https://mlsanity.com/paper/2311.14213/) (2023)
- [Fast Differentiable Modal Simulation of Non-linear Strings, Membranes, and Plates](https://mlsanity.com/paper/2505.05940/) (2025)
- [A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation](https://mlsanity.com/paper/1809.05483/) (2018)

## Abstract (arXiv)

We present a simulation-trained, non-iterative estimator for Task A of the 1st DAFx Parameter Estimation Challenge. Each unnormalized plate-reverb impulse response is summarized by amplitude, spectral, and decay descriptors, and an ensemble of tree regressors estimates the six target parameters in one pass. Across two independent synthetic validation sets, the normalized models outperform the training-set mean and an earlier raw-regression baseline. On a shared set, the final ensemble also outperforms a single run of the official default PSO at substantially lower inference cost. Since the official labels are hidden, parameter accuracy is measured on simulator-matched data, and the released responses support only audio-side consistency checks. The estimator returns point estimates without uncertainty.