Download Simulation-based Inference Plate Reverberation Inverse Problems 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.
Download DAFx Challenge Introduction & Results The 1st DAFx Parameter Estimation Challenge is an open initiative to advance the state of the art in parameter estimation for acoustic modeling. Stated as a system identification problem, this first edition focuses on plate reverberation—an archetypal dense, modal and weakly damped acoustic system. Participants tackled two tasks: (A) estimating the physical parameters of a vibrating plate from its impulse response, and (B) recovering the modal parameters of the same system. Both rest on a simulation framework based on the damped Kirchhoff–Love plate equation, and both are posed and scored entirely on synthetic data produced by that framework: no measurement of a real plate is involved. Two participants solved Task A down to machine precision by different strategies: one a neural network trained on a very large dataset, and one gradient-free optimization with many inexpensive evaluations. Task B proved considerably harder: the best submission attains a relative error of 0.33 on a [0, 2] scale, and every method recovers modal frequencies and decay rates far more accurately than modal gains. A complementary frequency-domain evaluation reorders the ranking and exposes a systematic gain bias to which the per-mode metric is blind.
Download Physical Model of the Chinese Yehu for Sound Synthesis 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.
Download A Corpus-Driven Parametric Modal Reverberator A parametric modal reverberator is presented in which synthesis parameters are derived from a large, curated corpus of room impulse responses (IRs). The collected responses are subjected to modal decomposition, yielding per-mode frequencies, damping coefficients, and residue amplitudes, together with a short early-reflection finite impulse response (FIR) filter. From the decomposed data, a feature table is constructed per IR comprising standard acoustic indices, per-band damping and density statistics, amplitude distributions, and FIR descriptors—50 variables in total. Six acoustically meaningful user controls are selected; since these exhibit substantial pairwise correlations across the corpus, they are orthogonalised via principal component analysis (PCA) prior to regression.
Download Shimmer Reverberation with Nonlinear Feedback Delay Networks Shimmer reverberation is an effect used in music production to deliver ethereal, pitch-shifted textures and evolving ambient soundscapes. This paper explores the synthesis of shimmer effects using the feedback delay network architecture, a popular real-time reverberator. We propose five distinct approaches for integrating nonlinear and time-varying operations into the feedback loop, focusing on expanding the harmonic content while adhering to energy-preservation and stability criteria. Our approach can generate a wide range of sonic characteristics, from harmonically rich distortions to musically coherent pitch-shifted reverberation, while maintaining stability and controllable decay behavior.
Download Diagonal Complex-Valued State Space Models for System Identification and Modeling of Metal Plate Reverbs 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.
Download A Unified Framework for Real-Time Concatenation-Driven Convolution This work introduces a novel framework for Concatenation-Driven Convolution (CDC), unifying concatenative synthesis and real-time convolution into a single integrated audio processing paradigm. While concatenative synthesis has traditionally been used for corpus-based sound generation and convolution has served as a largely static filtering technique, the proposed approach reconceptualizes impulse responses (IRs) as dynamic, navigable sonic material. In the CDC framework, a corpus of audio segments is analyzed using perceptual features and organized via a self-organizing map (SOM), enabling intuitive, gesture-based traversal of a structured timbral space; the resulting concatenative output is treated as a continuously evolving impulse response and injected directly into a partitioned convolution engine. Its central technical contribution is single-engine frequency-domain kernel interpolation: rather than crossfading the outputs of two convolution engines, the FFT-domain kernels of the current and target IRs are interpolated within a single engine, preserving the internal convolution state across IR transitions and avoiding the warm-up energy loss inherent to dual-engine crossfading.