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arXiv · 2611.02083

Count-Density Networks for Modal Plate Parameter Estimation

Rodrigo Diaz·Pablo Tablas De Paula·David Marttila·Ilias Ibnyahya·Chin-Yun Yu
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Abstract

We describe two submissions to Task B of the 1st DAFx Parameter Estimation Challenge, which estimates an unknown number of modal frequency, decay, and gain triples from a synthetic plate-reverb impulse response. The first method combines pooled spectral features with time-domain and absolute-scale conditioning in a real-valued convolutional count-density network, while the second uses a complex-valued Transformer count-density network. Both methods jointly infer the modal count and per-mode attributes directly from the IR. On an independently generated 100-IR comparison set, the two neural estimators achieve lower overall challenge error than the evaluated classical baselines, with frequency and decay estimation substantially more accurate than gain estimation.

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Rodrigo Diaz, Pablo Tablas De Paula, David Marttila, Ilias Ibnyahya, Chin-Yun Yu. 2026-11-01. Count-Density Networks for Modal Plate Parameter Estimation. https://arxiv.org/abs/2611.02083

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