Paper Archive

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.

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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.

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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.

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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.

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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.

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Plate reverberation is characterized by a dense distribution of resonant modes, which makes the estimation of modal parameters from observed responses a challenging inverse problem. To address this problem, we propose a physics guided dual stream framework that integrates an Audio Spectrogram Transformer (AST) with Dynamic Mode Decomposition (DMD). The AST branch models the global temporal and spectral structure of the impulse response, whereas the DMD branch extracts local descriptors associated with modal dynamics. The resulting representations are fused and processed by convolutional prediction heads to jointly estimate mode presence and the corresponding modal parameters. Experiments on the official validation set of Task B in the DAFx Challenge show that the proposed method reduces the overall relative error from 1.976 for the official baseline to 0.867. These results demonstrate that integrating local dynamic information derived from physical modeling with global transformer based representations substantially improves plate modal parameter estimation.

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The design of Virtual Analog (VA) algorithms has traditionally been divided between white-box (physics-based) and black-box (data-driven) approaches. Recent work has shown that hybrid methods, combining physical modeling with neural networks, can effectively leverage the strengths of both paradigms. In particular, Wave Digital Filters (WDFs) can be coupled with Multi-Layer Perceptrons (MLPs) to model circuits with multiple nonlinearities in a fully explicit manner. In this paper, we present a comparative study investigating the use of Kolmogorov-Arnold Networks (KANs) for VA modeling within the WDF framework. Unlike MLPs, KANs shift the learning paradigm by parameterizing activation functions instead of relying exclusively on learned weight matrices, potentially enabling more compact representations. Results show that, for our case study, KANs achieve accuracy comparable to MLPs while requiring approximately 70% fewer parameters at the cost of increased computational complexity. These findings suggest that KANs may represent a promising alternative in scenarios where memory footprint is a primary constraint, such as embedded audio applications, or when target models feature numerous nonlinear elements.

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Wave Digital Filters are a circuit-modeling paradigm well-suited for reusable software implementation, but existing software implementations often incur significant overhead due to run-time abstractions and data layout constraints. This paper presents a performance-oriented toolchain for implementing Wave Digital circuit models based on static code generation. The toolchain consists of a declarative circuit description language, a compiler that generates circuit simulation code with minimal persistent state and no run-time abstraction, and a minimal runtime library implementing specialized circuit components as Wave Digital Filters. Performance measurements across several test circuits demonstrate that the generated models consistently outperform existing implementations, and achieve near-ideal performance relative to a theoretical execution bound.

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Time-varying virtual analog filters used in digital audio effects and synthesizers are often implemented by discretizing continuous-time state-space systems using trapezoidal integration. When filter parameters such as cutoff frequency or resonance vary over time, as is the norm in musical applications, proving BIBO stability of the resulting time-varying discrete-time system becomes nontrivial. In this paper, we review the technique of common quadratic Lyapunov functions (CQLFs) from the control systems literature and show how a continuous-time CQLF is preserved through discretization. This allows us to prove stability of some time-varying virtual analog filters by working in the often simpler continuous-time domain. We apply this framework to several filters of musical interest, providing new proofs of stability for the state variable filter and the Sallen-Key filter, and new bounds on the stable time-varying parameter range for the Moog ladder and diode ladder filters.

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Digital Moog ladder filters are often compared by their linear frequency response, even though musically important differences emerge under nonlinear operation. This paper presents an open, reproducible SPICE-referenced evaluation framework that combines two components: a cross-implementation comparison of five digital ladder-filter implementations against the same SPICE reference, and a controlled common-core ablation. The cross-implementation comparison shows that close linear agreement can mask substantial nonlinear divergence, while the ablation shows that ladder nonlinear behavior depends not only on saturator choice and nonlinearity placement, but also on the non-uniform contribution of different ladder stages: in partial ablations of a SPICE-referenced TPT/ZDF core, retaining earlier-stage nonlinearities preserves harmonic behavior better than retaining later-stage nonlinearities alone. Beyond the specific comparisons reported here, the framework provides a reproducible basis for evaluating fidelity–cost trade-offs when nonlinear structure must be simplified.

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