A Differentiable Neural Network Approach To Parameter Estimation Of Reverberation
In Proceedings of the Sound and Music Computing Conference, 2022
Abstract
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.
Info
Conference Paper, 2022
In Proceedings of the Sound and Music Computing Conference, 2022
UN SDG Classification
DK Main Research Area
Science/Technology
