Band-Count Dense Modal Estimation with Fixed-Frequency Differentiable Resonator Refinement

Abstract: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.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)

arXiv:2608.00667 [eess.AS]

(or arXiv:2608.00667v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2608.00667 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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