2025/11/08 by Steven Atkinson, Atkinson, Steven
Computer Science · Physics and Astronomy · #Music Technology and Sound Studies #Generative Adversarial Networks and Image Synthesis #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2511.07470
This work demonstrates "slimmable Neural Amp Models", whose size and computational cost can be changed without additional training and with negligible computational overhead, enabling musicians to easily trade off between the accuracy and compute of the models they are using. The method's performance is quantified against commonly-used baselines, and a real-time demonstration of the model in an audio effect plug-in is developed.