发表机构
Queen Mary University of London（伦敦玛丽女王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对密集板混响脉冲响应的模态估计问题，训练ExtraTrees回归器预测频带模态数，结合可微分全极点谐振器优化参数，使挑战赛误差较基线降低约66%。
AI 中文摘要
第一届DAFx参数估计挑战赛的任务B要求估计密集板混响脉冲响应中的频率、衰减率、增益和模态数量。弱模态和重叠模态使得稀疏峰值检测容易出现严重的计数不足。我们在模拟器生成的数据上训练ExtraTrees回归器，以预测四个频带中的模态数量。这些计数定义了密集频率网格，随后，一个可微分全极点谐振器模型在保持频率固定的同时优化衰减和增益。在两个独立的合成验证集上，该系统相对于官方默认峰值拾取基线，将局部挑战赛风格误差降低了约66%。该改进主要与更低的模态计数不匹配相关，而衰减和增益仍然是最大的误差来源。这些发现支持将模态密度估计与连续参数拟合相分离。
英文摘要
Task B of the 1st DAFx Parameter Estimation Challenge requires estimating the frequencies, decay rates, gains, and number of modes in a dense plate-reverb impulse response. Weak and overlapping modes make sparse peak detection prone to severe undercounting. We train an ExtraTrees regressor on simulator-generated data to predict mode counts in four frequency bands. These counts define dense frequency grids, after which a differentiable all-pole resonator model refines decay and gain while keeping frequency fixed. On two separate synthetic validation sets, the system reduces a local challenge-style error by about 66% relative to the official default peak-picking baseline. The improvement is mainly associated with lower mode-count mismatch, while decay and gain remain the largest error sources. These findings support separating modal-density estimation from continuous parameter fitting.
CommentsAccepted as a challenge paper at the 29th International Conference on Digital Audio Effects (DAFx 2026), Cambridge, MA, USA, September, 2026
