Download Gradient Descent Optimization of Room Impulse Responses with Parameter-Efficient Differentiable Feedback Delay Networks Artificial reverberation can be produced either by convolving a signal with a measured room impulse response (RIR) or by synthesizing it with a parametric algorithm such as a Feedback Delay Network (FDN). The former reproduces a captured space faithfully but is costly to run and offers no control over its acoustic properties, while the latter is efficient and editable but hard to match to a specific room. In this paper we bridge the two by fitting a fully differentiable FDN to a measured RIR through gradient descent. The proposed network uses sixteen delay lines at a sampling rate of 48 kHz and trains all of its components jointly, including the delay lengths, the feedback matrix, the early-reflection taps, and a set of attenuation filters that control the frequency-dependent decay.
Download Group Delay Manipulation for Creative Sound Transformation with the Giant FFT The Giant FFT is a single DFT spanning an entire audio file that produces a spectrum encoding the complete temporal evolution of a sound. Creative manipulations in this domain have produced compelling results, but typically smear discrete events into sustained textures by disrupting the temporal relationships between frequency bins. This paper introduces a framework for coherent spectral manipulation in the "group delay domain", where the derivative of the phase spectrum with respect to frequency makes the temporal center of gravity of spectral energy explicit at every frequency bin. By identifying spectral regions around amplitude peaks and grouping them by group delay similarity, spectral features can be displaced in time through uniform modification of their group delay.
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 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 Modal Structure of Plate Boundaries and Klein Bottle Reverberation 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.
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 Non-iterative Modal Parameter Estimation for Plate Reverbs via Matrix-Pencil-Guided State Space Model Initialization 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.
Download ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution This paper is a technical report on the methodology submitted for Task A of the 1st DAFx Parameter Estimation Challenge. The goal of the challenge’s task is to invert the multi-dimensional physical and geometric parameters of a virtual plate reverberator given a target reference impulse response. To achieve this, we present a multi-stage gradient-free optimization framework. This three-stage optimization is computed using an efficient physics-based simulator, starting with an optimization of only mode frequency-determining physical parameters, followed by a 6-DoF parameter optimization with position-determining ones and a final phase for frequency- and position-independent mode amplitude estimation.
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 Transformer-Based Plate Parameter Estimation with Differentiable and Particle-Swarm Refinement We present two Transformer-based methods for Task A of the 1st DAFx Parameter Estimation Challenge, which requires estimating six effective physical parameters of a synthetic plate-reverb model from its impulse response (IR). Method A1 combines an Audio Spectrogram Transformer encoder and Transformer regressor with differentiable IR refinement. Method A2 uses the same encoder to condition a continuous normalizing flow and refines sampled candidates using particle swarm optimisation (PSO) and gradient polishing. Both methods preserve the absolute IR scale to recover surface density. On a synthetic holdout set of 100 IRs, both refinement procedures reduce waveform and parameter errors by more than three orders of magnitude relative to the unrefined neural outputs. The PSO-based pipeline achieves the lowest errors, indicating near-perfect recovery in this matched synthetic setting.