Download Modeling the Frequency-Dependent Sound Energy Decay of Acoustic Environments with Differentiable Feedback Delay Networks Differentiable machine learning techniques have recently proved effective for finding the parameters of Feedback Delay Networks (FDNs) so that their output matches desired perceptual qualities of target room impulse responses. However, we show that existing methods tend to fail at modeling the frequency-dependent behavior of sound energy decay that characterizes real-world environments unless properly trained. In this paper, we introduce a novel perceptual loss function based on the mel-scale energy decay relief, which generalizes the well-known time-domain energy decay curve to multiple frequency bands. We also augment the prototype FDN by incorporating differentiable wideband attenuation and output filters, and train them via backpropagation along with the other model parameters. The proposed approach improves upon existing strategies for designing and training differentiable FDNs, making it more suitable for audio processing applications where realistic and controllable artificial reverberation is desirable, such as gaming, music production, and virtual reality.
Download Parameter Estimation via Differentiable Modal Plate Synthesis 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.
Download Training Neural Models of Nonlinear Multi-Port Elements Within Wave Digital Structures Through Discrete-Time Simulation Neural networks have been applied within the Wave Digital Filter
(WDF) framework as data-driven models for nonlinear multi-port
circuit elements. Conventionally, these models are trained on wave
variables obtained by sampling the current-voltage characteristic
of the considered nonlinear element before being incorporated into
the circuit WDF implementation. However, isolating multi-port
elements for this process can be challenging, as their nonlinear
behavior often depends on dynamic effects that emerge from interactions with the surrounding circuit. In this paper, we propose a
novel approach for training neural models of nonlinear multi-port
elements directly within a circuit’s Wave Digital (WD) discretetime implementation, relying solely on circuit input-output voltage
measurements. Exploiting the differentiability of WD simulations,
we embed the neural network into the simulation process and optimize its parameters using gradient-based methods by minimizing
a loss function defined over the circuit output voltage. Experimental results demonstrate the effectiveness of the proposed approach
in accurately capturing the nonlinear circuit behavior, while preserving the interpretability and modularity of WDFs.
Download Differentiable MIMO Feedback Delay Networks for Multichannel Room Impulse Response Modeling Recently, with the advent of new performing headsets and goggles, the demand for Virtual and Augmented Reality applications has experienced a steep increase. In order to coherently navigate the virtual rooms, the acoustics of the scene must be emulated in the most accurate and efficient way possible. Amongst others, Feedback Delay Networks (FDNs) have proved to be valuable tools for tackling such a task. In this article, we expand and adapt a method recently proposed for the data-driven optimization of single-inputsingle-output FDNs to the multiple-input-multiple-output (MIMO) case for addressing spatial/space-time processing applications. By testing our methodology on items taken from two different datasets, we show that the parameters of MIMO FDNs can be jointly optimized to match some perceptual characteristics of given multichannel room impulse responses, overcoming approaches available in the literature, and paving the way toward increasingly efficient and accurate real-time virtual room acoustics rendering.
Download A Comparative Study of Kolmogorov-Arnold Networks and Multi-Layer Perceptrons for Virtual Analog Modeling in Wave Digital Filters 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.
Download Modeling the Impulse Response of Higher-Order Microphone Arrays Using Differentiable Feedback Delay Networks Recently, differentiable multiple-input multiple-output Feedback
Delay Networks (FDNs) have been proposed for modeling target multichannel room impulse responses by optimizing their parameters according to perceptually-driven time-domain descriptors. However, in spatial audio applications, frequency-domain
characteristics and inter-channel differences are crucial for accurately replicating a given soundfield. In this article, targeting the
modeling of the response of higher-order microphone arrays, we
improve on the methodology by optimizing the FDN parameters
using a novel spatially-informed loss function, demonstrating its
superior performance over previous approaches and paving the
way toward the use of differentiable FDNs in spatial audio applications such as soundfield reconstruction and rendering.
Download Wave Digital Model of the MXR Phase 90 Based on a Time-Varying Resistor Approximation of JFET Elements Virtual Analog (VA) modeling is the practice of digitally emulating analog audio gear. Over the past few years, with the purpose of recreating the alleged distinctive sound of audio equipment and musicians, many different guitar pedals have been emulated by means of the VA paradigm but little attention has been given to phasers. Phasers process the spectrum of the input signal with time-varying notches by means of shifting stages typically realized with a network of transistors, whose nonlinear equations are, in general, demanding to be solved. In this paper, we take as a reference the famous MXR Phase 90 guitar pedal, and we propose an efficient time-varying model of its Junction Field-Effect Transistors (JFETs) based on a channel resistance approximation. We then employ such a model in the Wave Digital domain to emulate in real-time the guitar pedal, obtaining an implementation characterized by low computational cost and good accuracy.
Download Explicit Wave Digital Model of the Fulltone OCD Pedal Based on Canonical Piecewise-Linear Functions 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.