Evaluating Neural Networks Architectures for Spring Reverb Modelling

TL;DR

Five different neural network architectures are compared, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.

Abstract

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.

Figures (7)

• Figure 1: Spring tank diagram.
• Figure 2: Spring reverberation unit diagram.
• Figure 3: Block diagram of the TCN architecture.
• Figure 4: Block diagram of the feed-forward WaveNet architecture.
• Figure 5: Block diagram of the GCN architecture.