2026/08/01 by Minhui Lu, Joshua D. Reiss
Engineering · Computer Science · #eess.AS #cs.LG
Accepted as a challenge paper at the 29th International Conference on Digital Audio Effects, Cambridge, MA, USA, 2026
arxiv created 2026/08/01 · arxiv updated 2026/08/04
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.