Paper Archive

The yehu is a Chinese bowed string instrument featuring a resonator carved from a coconut shell, a seashell-based bridge, and two silk strings. This paper proposes a physical model of the yehu and reports on simulations using a finite-difference scheme with measurement-based physical characterization. The proposed model consists of two stiff strings coupled at the bridge, a bow with elastic bow hairs, a stopping finger, and a modal model of the bridge. A non-iterative solver based on energy quadratization is used to model the finger–string contact force, while an iterative solver is used for elasto-plastic bow-string friction force. The bridge-body model is based on a modal characterization obtained from the measured bridge admittance. The measured radiation transfer function is represented as a bank of parallel second-order filters and is applied to the simulated bridge force to incorporate body radiation characteristics. Finally, computational performance tests are conducted, showing that the proposed model is capable of real-time computation.

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Physical modeling sound synthesis has achieved remarkable success in terms of its fidelity to reality. In many cases, since modeling of the physical system is performed on the sounding objects that already exist in the real world, observation precedes the model itself. Departing from this convention, this paper aims to physically model the acoustic characteristics of objects that do not necessarily exist in reality. Specifically, we study wave propagation on compact two-dimensional (2D) manifolds that are non-orientable surfaces, such as the Klein bottle that cannot be embedded in three-dimensional Euclidean space without self-intersection. We derive closed-form expressions for the eigenfrequencies and mode shapes of non-orientable 2D topologies and study their acoustic characteristics. The modal structures are verified through comparison with finite-difference time-domain simulations. The results demonstrate how the topological character formed by the boundaries influences the acoustic resonances, and how the quotient-space framework provides a practical route to reverb synthesis on geometries with no physical counterpart.

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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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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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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 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).

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WaveNet-style convolutional networks emulate tube amplifiers and distortion pedals with high fidelity, but their computational cost has confined them to desktops or dedicated DSP hardware. We present a sparse-enabled WaveNet inference engine for iOS that runs heavily pruned neural guitar amplifier models in real time on iPhones. Aggressive iterative magnitude pruning removes 90% of the network weights with no perceptible loss in quality. A custom sparse C++ engine turns this sparsity directly into compute savings, sustaining low-latency real-time operation on a CPU-only iPhone implementation where the dense model cannot. On-device output matches the trained model to within int16 quantization error. At the demonstration, visitors will play a guitar through the app on iPhone hardware and A/B the on-device pruned model against the physical pedal it emulates. Source code and audio examples are available online.

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Accurate and interpretable modeling of plate reverbs remains an important challenge in virtual analog modeling of audio effects. While existing neural network-based black-box approaches already achieve high-quality synthesis and strong perceptual quality, they often lack the possibility to identify the underlying physically meaningful complex, long-memory modal behavior. In this work, we address this limitation by proposing a restricted complex-valued diagonal State Space Model (SSM), showing its equivalence to a parallel second-order all-pole filter, also utilizing efficient training via parallel state computation using the parallel scan algorithm. Additionally, we propose a Matrix Pencil (MP) guided eigenvalue initialization, improving synthesis quality and system identification performance.

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Modal parameter identification for plate reverbs remains a challenging problem in virtual-analog audio effect emulation. Though neural network-based black-box approaches achieve high modeling accuracy, they generally lack interpretability and do not provide access to physically meaningful modal parameters. In this work, we present our solution to Task B of the DAFx Plate Reverb Parameter Estimation Challenge. Our method first estimates the total number of modes and then employs a Matrix Pencil (MP)-guided eigenvalue initialization strategy for a diagonal complex-valued State Space Model (SSM), which can be interpreted as a bank of parallel second-order all-pole filters. Exploiting the linearity of the resulting system, we compute the state impulse responses and replace gradient-based optimization with a closed-form least-squares estimation of the modal gains. The proposed approach enables accurate recovery of the modal parameters while maintaining an interpretable system representation.

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We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse response. To do so, we use the Simulation-Based Inference (SBI) framework, in which we train a neural network to estimate a density over plate parameters given an impulse response, using a dataset generated by the simulator. Inference for a new impulse response then requires only a forward pass through the network, without involving the simulator. For each test observation, we fine-tune a specific network: additional simulation rounds are performed by sampling parameters from the current estimated distribution, simulating the corresponding impulse responses, and fine-tuning to produce the specialized network.

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