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 Scattering Delay Networks for Artificial Reverberation Scattering delay networks (SDNs) provide a flexible and efficient
framework for artificial reverberation and room acoustic modeling. In this work, we introduce a differentiable SDN, enabling
gradient-based optimization of its parameters to better approximate the acoustics of real-world environments. By formulating
key parameters such as scattering matrices and absorption filters
as differentiable functions, we employ gradient descent to optimize an SDN based on a target room impulse response. Our approach minimizes discrepancies in perceptually relevant acoustic
features, such as energy decay and frequency-dependent reverberation times. Experimental results demonstrate that the learned SDN
configurations significantly improve the accuracy of synthetic reverberation, highlighting the potential of data-driven room acoustic modeling.
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.