Download Transformer-Based Plate Parameter Estimation with Differentiable and Particle-Swarm Refinement We present two Transformer-based methods for Task A of the 1st DAFx Parameter Estimation Challenge, which requires estimating six effective physical parameters of a synthetic plate-reverb model from its impulse response (IR). Method A1 combines an Audio Spectrogram Transformer encoder and Transformer regressor with differentiable IR refinement. Method A2 uses the same encoder to condition a continuous normalizing flow and refines sampled candidates using particle swarm optimisation (PSO) and gradient polishing. Both methods preserve the absolute IR scale to recover surface density. On a synthetic holdout set of 100 IRs, both refinement procedures reduce waveform and parameter errors by more than three orders of magnitude relative to the unrefined neural outputs. The PSO-based pipeline achieves the lowest errors, indicating near-perfect recovery in this matched synthetic setting.
Download Count-Density Networks for Modal Plate Parameter Estimation 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.