Evaluating Neural Networks Architectures for Spring Reverb Modelling
This work addresses the problem of improving black-box modelling techniques for spring reverberation in audio processing, which is incremental as it compares existing neural architectures rather than introducing a new paradigm.
The study tackled the challenge of digitally emulating spring reverb, a nonlinear audio effect, by comparing five neural network architectures, including convolutional and recurrent models, to replicate its characteristics, achieving evaluations on datasets at 16 kHz and 48 kHz sampling rates.
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.