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arXiv · 2610.28100

Transformer-Based Plate Parameter Estimation with Differentiable and Particle-Swarm Refinement

David Marttila·Rodrigo Diaz·Pablo Tablas De Paula·Ilias Ibnyahya·Chin-Yun Yu
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Abstract

We present two Transformer-based methods for Task A of the 1st DAFx Parameter Estimation Challenge, which requires estimating six effective physical parameters of a synthetic plate-reverb model from its impulse response (IR). Method A1 combines an Audio Spectrogram Transformer encoder and Transformer regressor with differentiable IR refinement. Method A2 uses the same encoder to condition a continuous normalizing flow and refines sampled candidates using particle swarm optimisation (PSO) and gradient polishing. Both methods preserve the absolute IR scale to recover surface density. On a synthetic holdout set of 100 IRs, both refinement procedures reduce waveform and parameter errors by more than three orders of magnitude relative to the unrefined neural outputs. The PSO-based pipeline achieves the lowest errors, indicating near-perfect recovery in this matched synthetic setting.

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David Marttila, Rodrigo Diaz, Pablo Tablas De Paula, Ilias Ibnyahya, Chin-Yun Yu. 2026-10-20. Transformer-Based Plate Parameter Estimation with Differentiable and Particle-Swarm Refinement. https://arxiv.org/abs/2610.28100

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