ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution

Jin Woo Lee, Jatin Chowdhury, Facundo Franchino, Soohyun Kim, Mark Rau

Proceedings of the 29th International Conference on Digital Audio Effects (DAFx26), Parameter Estimation Challenge, Task A, Cambridge, MA, USA, 1–4 September 2026

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Abstract

This paper is a technical report on the methodology submitted for Task A of the 1st DAFx Parameter Estimation Challenge. The goal of the challenge’s task is to invert the multi-dimensional physical and geometric parameters of a virtual plate reverberator given a target reference impulse response. To achieve this, we present a multi-stage gradient-free optimization framework. This three-stage optimization is computed using an efficient physics-based simulator, starting with an optimization of only mode frequency-determining physical parameters, followed by a 6-DoF parameter optimization with position-determining ones and a final phase for frequency- and position-independent mode amplitude estimation.