Peak-Residual Modal Estimation with Learned Calibration and High-Band Density Correction ★
This paper describes two related submissions to Task B of the 1st DAFx Parameter Estimation Challenge. Both estimate modal frequency, decay, and gain directly from an unnormalised plate impulse response without using plate parameters, the excluded analytical modal-frequency law, or official-test ground truth. The primary system constructs a large candidate pool through prominence-graded spectral peak picking, iterative residual analysis, multi-view consensus, band-wise budgeting, and a learned file-level mode-count target. Raw decay and gain estimates are then corrected by a small mode-wise neural network that is not allowed to move frequencies or change the number of rows. A secondary variant addresses suspected high-frequency under-counting with a separately gated, non-oracle density-fill stage in the 6–10 kHz band. The paper reports development diagnostics, reproducibility information, and descriptive statistics for the 16 official outputs. The two variants expose a deliberate precision–recall trade-off: one preserves a visible spectral justification for every row, while the other tests bounded hidden-multiplicity augmentation in densely overlapped regions.