3 papers
cs.SD2026
Time-Varying Audio Effect Modeling by End-to-End Adversarial Training
Yann Bourdin, Pierrick Legrand, Fanny Roche
Deep learning has become a standard approach for the modeling of audio effects, yet strictly black-box modeling remains problematic for time-varying systems. Unlike time-invariant…
cs.NE2026
Evolutionary Algorithm for Reservoir Learning and Yielding
Julien Testu, Pierrick Legrand, Xavier Hinaut
Reservoir computing, a type of recurrent neural network, is a promising approach for temporal learning as it separates dynamic processing from the trained readout layer. However, c…
cs.LG2025
Empirical Results for Adjusting Truncated Backpropagation Through Time while Training Neural Audio Effects
Yann Bourdin, Pierrick Legrand, Fanny Roche
This paper investigates the optimization of Truncated Backpropagation Through Time (TBPTT) for training neural networks in digital audio effect modeling, with a focus on dynamic ra…