Differentiable Digital Signal Processing is a library and set of machine learning tools that disentangle the loudness and pitch of an audio signal for timbre transfer or for applying digital audio effects. This paper presents a DDSP-based neural network that incorporates a feedback delay network plugin written in JUCE in an audio processing layer, with the purpose of tuning a large set of reverberator parameters to emulate the reverb of a target audio signal. We first describe the implementation of the proposed network, together with its multiscale loss. We then report two experiments that try to tune the reverberator plugin: a "dark" reverb where the filters are set to cut frequencies in the middle and high range, and a "brighter", more metallic sounding reverb with less damping. We conclude with the observations about advantages and shortcomings of the neural network.
|Titel||Proceedings of the 19th Sound and Music Computing Conference, June 5-12th, 2022, Saint-Étienne (France) : SMC/JIM/IFC 2022|
|Redaktører||Romain Michon, Laurent Pottier, Yann Orlarey|
|Forlag||Sound and Music Computing Network|
|Status||Udgivet - 2022|
|Begivenhed||19th Sound and Music Computing Conference, SMC 2022 - Saint-Etienne, Frankrig|
Varighed: 5 jun. 2022 → 12 jun. 2022
|Konference||19th Sound and Music Computing Conference, SMC 2022|
|Periode||05/06/2022 → 12/06/2022|
|Navn||Proceedings of the Sound and Music Computing Conference|
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Copyright: © 2022 Søren Vøgg Lyster et al.