Evaluating neural networks architectures for spring reverb modelling

Papaleo, Francesco; Lizarraga-Seijas, Xavier; Font, Frederic

Evaluating neural networks architectures for spring reverb modelling

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Citation

Papaleo F, Lizarraga-Seijas X, Font F. Evaluating neural networks architectures for spring reverb modelling. In: Sena E, Mannall J, editors. Proceedings of the 27th International Conference on Digital Audio Effects (DAFx24); 2024 Sep 3-7; Guildford, United Kingdom. [Guildford]: University of Surrey; 2024. p. 302-9.

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.