Evaluating Neural Networks Architectures for Spring Reverb Modelling
Abstract
Reverberation is a key element in spatial audio perception, histor- ically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal process- ing 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 net- work 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 sam- pling 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 tech- niques in the domain of spring reverberation.
Audio Examples collected during the evaluation
| Model | Target | Prediction |
|---|---|---|
| GCN | ||
| TCN | ||
| WaveNet | ||
| GRU | ||
| LSTM |
Audio Examples with other kind of sounds obtained at inference
| Sound | Dry | GCN | TCN | WaveNet | GRU | LSTM |
|---|---|---|---|---|---|---|
| Drums | ||||||
| Synth |
Bibtex Citation
@inproceedings{DAFx24_paper_77,
author = "Papaleo, Francesco and Lizarraga-Seijas, Xavier and Font, Frederic",
title = "{Evaluating Neural Networks Architectures for Spring Reverb Modelling}",
booktitle = "Proceedings of the 27-th Int. Conf. on Digital Audio Effects (DAFx24)",
editor = "De Sena, E. and Mannall, J.",
location = "Guildford, Surrey, UK",
eventdate = "2024-09-03/2024-09-07",
year = "2024",
month = "Sept.",
publisher = "",
issn = "2413-6689",
doi = "",
pages = ""
}
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