Accurate Plate Reverb Parameter Estimation Using Two-Stage Evolutionary Search

2026-08-28

AI Summary

本文提出一种两阶段进化搜索方法，通过CMA-ES和三元搜索估计金属板混响器的六个物理参数，以解决从单个脉冲响应中准确恢复参数的问题。

Abstract

We describe our submission to Task A of the 1st DAFx parameter estimation challenge. The task is to recover the six physical parameters of a simulated metal-plate reverberator -- its dimensions and material properties -- from a single impulse response (IR). We treat this as a black-box optimization: candidate parameter sets are fed to the simulator and scored by a loss against the target IR. The method has two stages. The first uses CMA-ES, an evolutionary optimizer, to recover five of the six parameters, comparing IRs under an amplitude-normalized loss. Amplitude normalization makes the search robust but discards the cue to the sixth parameter, the plate's surface density; a second stage therefore estimates it alone, with a ternary search on the un-normalized loss. As the choice of loss strongly affects the search, we select it beforehand, and analyze why compression in the common multi-scale spectral loss degrades recovery. Finally, we test our method on a validation set of 50 IRs, discuss a pathological failure mode, and ablate to justify having two different stages instead of a unified CMA-ES search.

Problem

• parameter estimation
• impulse response
• reverberator
• evolutionary search
• black-box optimization

Innovation

• Two-Stage Evolutionary Search
• CMA-ES
• Amplitude Normalization
• Ternary Search
• Multi-scale Spectral Loss