Download Evaluating Neural Networks Architectures for Spring Reverb Modelling Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.
Download A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds
characterized by stochastic structure and perceptual stationarity.
Drawing inspiration from the statistical and perceptual framework
of McDermott and Simoncelli, TexStat identifies similarities
between signals belonging to the same texture category without
relying on temporal structure. We also propose using TexStat
as a validation metric alongside Frechet Audio Distances (FAD) to
evaluate texture sound synthesis models. In addition to TexStat,
we present TexEnv, an efficient, lightweight and differentiable
texture sound synthesizer that generates audio by imposing amplitude envelopes on filtered noise. We further integrate these components into TexDSP, a DDSP-inspired generative model tailored
for texture sounds. Through extensive experiments across various
texture sound types, we demonstrate that TexStat is perceptually meaningful, time-invariant, and robust to noise, features that
make it effective both as a loss function for generative tasks and as
a validation metric. All tools and code are provided as open-source
contributions and our PyTorch implementations are efficient, differentiable, and highly configurable, enabling its use in both generative tasks and as a perceptually grounded evaluation metric.