用仿真数据训练的非迭代模型，一键估计混响参数。
Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response
- 基于仿真数据训练树模型，一次性预测六个混响参数。
- 在两个合成数据集上优于均值和原始回归基线。
- 推理成本远低于官方优化器，适合实时应用。
我们提出一种针对第一届DAFx参数估计挑战赛任务A的仿真训练、非迭代估计器。每个未归一化的板式混响脉冲响应通过幅度、频谱和衰减描述符进行表征，一组树回归器在一次计算中估计六个目标参数。在两个独立的合成验证集上，归一化模型的表现优于训练集均值和早期的原始回归基线。在一个共享数据集上，最终集成模型的表现也优于单次运行的官方默认粒子群优化（PSO），且推理开销显著更低。由于官方标签不可见，参数准确性在模拟器匹配的数据上评估，发布的响应仅支持音频一致性检查。该估计器返回点估计，不提供不确定性信息。
原文摘要 · Abstract (English)
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.
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