Paper Archive

Modeling violin bridge admittance is a long-standing problem in musical acoustics, with applications in sound analysis, synthesis, and virtual instrument design. In this work, we investigate the use of the Eigensystem Realization Algorithm (ERA) for deriving reduced-order state-space models directly from measured impulse responses. The proposed approach allows us to extract dominant system dynamics and obtain compact realizations without requiring explicit modal parameterization. We evaluate ERA on a dataset of modern and historical violins and compare it against established modal and state-space identification methods. Experimental results demonstrate that ERA outperforms existing approaches by achieving lower reconstruction errors in both the time and frequency domains while preserving perceptually relevant characteristics of the bridge response. Furthermore, we show that the state-space realizations obtained using ERA reproduce the target frequency-dependent energy decay more accurately than models obtained using the baseline methods. These findings support the use of ERA as an efficient and flexible alternative for modeling violin bridge admittances, with applications that span from audio synthesis and processing to instrument virtualization.

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Virtual Analog (VA) modeling aims at digitally emulating analog audio equipment while preserving its characteristic nonlinear behavior and musical expressiveness. In the context of guitar effects, overdrive pedals represent a cornerstone of many signal chains, as they strongly contribute to the perceived dynamics, articulation, and timbral identity of the instrument. Among these, the Fulltone OCD overdrive is considered a standard in both studio and live environments, being widely adopted across rock and metal genres. In this article, we present an explicit Wave Digital (WD) model of the Fulltone OCD (v2) pedal. By exploiting the circuit topology, the MOSFETs and the germanium diode composing the asymmetric clipping stage are grouped into a single equivalent nonlinear element, enabling an explicit WD realization that avoids costly iterative solvers. The resulting nonlinear characteristic is approximated by means of a Canonical Piecewise-Linear (CPWL) function, yielding a compact and efficient explicit model suitable for real-time implementation. The proposed model is validated against reference simulations and implemented both in MATLAB and as a real-time audio plug-in using the JUCE framework.

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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.

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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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