Modeling plate and spring reverberation using a DSP-informed deep neural network
This work addresses the challenge of accurately simulating artificial reverberators for audio production, offering a novel approach that could enhance music processing tools, though it is incremental in applying deep learning to a specific domain.
The authors tackled the problem of modeling plate and spring reverberators, which are complex electromechanical audio systems, by proposing a DSP-informed deep neural network architecture, achieving perceptual evaluation results that demonstrate the model's effectiveness in learning their nonlinear responses.
Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Nowadays they are often used in music production for aesthetic reasons due to their particular sonic characteristics. The modeling of these audio processors and their perceptual qualities is difficult since they use mechanical elements together with analog electronics resulting in an extremely complex response. Based on digital reverberators that use sparse FIR filters, we propose a signal processing-informed deep learning architecture for the modeling of artificial reverberators. We explore the capabilities of deep neural networks to learn such highly nonlinear electromechanical responses and we perform modeling of plate and spring reverberators. In order to measure the performance of the model, we conduct a perceptual evaluation experiment and we also analyze how the given task is accomplished and what the model is actually learning.