通过分频段预测模式数，显著提升混响信号的模式估计精度。
Band-Count Dense Modal Estimation with Fixed-Frequency Differentiable Resonator Refinement
- 分四频段用随机森林回归预测模式数量，构建密集频率网格。
- 在固定频率下用可微全极点谐振器优化衰减率与增益，误差降低66%。
- 适合需要高精度混响建模的音频处理研究者，尤其关注模式密度估计。
DAFx 参数估计挑战赛第1项任务要求估计密集板混响脉冲响应中的频率、衰减速率、增益及模式数量。由于模式弱且重叠，稀疏峰值检测易导致严重漏检。我们利用模拟生成数据训练了一个 ExtraTrees 回归器，预测四个频段内的模式数量。基于这些数量构建密集频率网格后，采用可微全极点谐振器模型在固定频率条件下优化衰减率和增益。在两个独立的合成验证集上，系统相较官方默认峰值检测基线，将局部挑战式误差降低了约66%。改进主要源于更低的模式数错配，而衰减率与增益仍是主要误差来源。结果支持将模式密度估计与连续参数拟合分离处理。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。