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.
Download Multi-View Subband Autoregressive Pole Harvesting for Modal Plate Identification We describe a Task B submission for the 1st DAFx Parameter Estimation Challenge. A matching-pursuit anchor stage seeds a multi-view subband autoregressive (AR) pole harvester on the raw IR and its first two finite differences; since linear filtering preserves pole locations while changing residues, the three views expose complementary subsets of the same pole set. Bands in which the AR order saturates are recursively split, and any remaining under-resolved region is completed from an IR-derived saturation indicator. Gains are assigned by a global ridge least-squares (LS) fit in the damped-biquad atom convention. The pipeline uses only the unnormalized IR; so it does not use plate parameters, analytical modal-frequency or decay laws, .wav files, or Task A information.
Download A Multi-Resolution Spectrogram Approach for Estimating the Physical Parameters of a Plate Reverb The ResNet-18 image classification model is employed to determine the physical parameters of a plate reverb from a recording of the impulse response. The model is adapted to derive parameters using normalized and down-sampled multi-resolution spectrograms computed from the provided impulse responses (IRs). To refine the prediction of the output location, the spectral phase response is also included as an additional input channel to the network since multiple output locations can give the same magnitude response for high-order resonant modes. On a 5000 IR validation set, our model achieves an average normalized mean squared error (NMSE) of 0.02920 across all parameters, with the lowest average NMSE occurring for parameters yo (0.00228), Ly (0.00347), and xo (0.00574).
Download Parameter Estimation via Differentiable Modal Plate Synthesis We present our submission to Task A of the 1st DAFx Parameter Estimation Challenge, which concerns the estimation of the physical parameters of a vibrating plate from a synthetic impulse response. Our approach introduces a differentiable modal plate synthesizer and estimates the plate parameters through inference-time gradient-based optimization of the synthesizer parameters. The six target parameters are recovered by minimizing a multi-scale spectral loss via backpropagation through the differentiable plate model. To handle the non-convexity of the loss landscape, we adopt a two-phase training strategy consisting of multiple short-term probe optimizations, followed by full-scale refinement initialized from the best candidate. We evaluate the approach on eight impulse responses synthesized with the official challenge dataset generator. Compared with a constant-value predictor and the particle swarm optimization baseline provided by the challenge, the proposed method reduces the prediction error by approximately one order of magnitude.
Download Accurate Plate Reverb Parameter Estimation Using Two-Stage Evolutionary Search We describe our submission to Task A of the 1st DAFx parameter estimation challenge. The task is to recover the six physical parameters of a simulated metal-plate reverberator—its dimensions and material properties—from a single impulse response (IR). We treat this as a black-box optimization: candidate parameter sets are fed to the simulator and scored by a loss against the target IR. The method has two stages. The first uses CMA-ES, an evolutionary optimizer, to recover five of the six parameters, comparing IRs under an amplitude-normalized loss. Amplitude normalization makes the search robust but discards the cue to the sixth parameter, the plate’s surface density; a second stage therefore estimates it alone, with a ternary search on the un-normalized loss. As the choice of loss strongly affects the search, we select it beforehand, and analyze why compression in the common multi-scale spectral loss degrades recovery. Finally, we test our method on a validation set of 50 IRs, discuss a pathological failure mode, and ablate to justify having two different stages instead of a unified CMA-ES search.
Download Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response ★ We present a simulation-trained, non-iterative estimator for Task A of the 1st DAFx Parameter Estimation Challenge. Each unnormalized plate-reverb impulse response is summarized by amplitude, spectral, and decay descriptors, and an ensemble of tree regressors estimates the six target parameters in one pass. Across two independent synthetic validation sets, the normalized models outperform the training-set mean and an earlier raw-regression baseline. On a shared set, the final ensemble also outperforms a single run of the official default PSO at substantially lower inference cost. Since the official labels are hidden, parameter accuracy is measured on simulator-matched data, and the released responses support only audio-side consistency checks. The estimator returns point estimates without uncertainty.
Download Neural Networks for Physical Parameter Estimation of Plate Reverberation from Impulse Responses ★ This paper presents our Task A submission to the 1st DAFx Parameter Estimation Challenge. We use the official ModalPlate dataset generator to synthesize 1000 one-second plate impulse responses with randomly sampled parameters inside the public ranges. A time-domain CNN-GRU regressor then estimates the six official Task A parameters from each unnormalised waveform. The model combines three one-dimensional convolutional blocks with a bidirectional gated recurrent unit and is trained with mean squared error on min-max normalised targets. The generated data are split into 700/150/150 train/validation/test examples, and the test split is never used during training or model selection. The implementation follows the official Task A format and exports evaluation-compatible prediction files for both development evaluation and blind-set submission.
Download Band-Count Dense Modal Estimation with Fixed-Frequency Differentiable Resonator Refinement ★ 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.
Download 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.
Download Physics-Inspired Feature Fusion for Plate Parameter Estimation from Acoustic Impulse Responses ★ Estimating physical plate parameters from impulse responses is a challenging inverse problem. Task A of the first Digital Audio Effects Parameter Estimation Challenge requires the recovery of six identifiable parameters from displacement impulse responses. In this work, we propose a physics-inspired feature fusion network (PIFFN) that combines a pretrained convolutional backbone with a 15-dimensional physics-inspired feature vector computed from the impulse response. These physics-inspired features describe amplitude scale, temporal decay, and spectral structure without relying on modal-distribution priors. The proposed model is evaluated on the official validation set, achieving an overall normalized mean squared error of 0.00362. Compared with the official particle swarm optimization baseline and backbone-only model, PIFFN shows a clear performance improvement, demonstrating its effectiveness for plate parameter estimation.