ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution

Jin Woo Lee; Jatin Chowdhury; Facundo Franchino; Soohyun Kim; Mark Rau
DAFx-2026 - Cambridge
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.
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