EG-VAE: A Unified Framework for Electric Guitar Tone Transfer and Removal

Abstract

Electric guitar tone transfer (EGTT) and tone removal (EGTR) are two fundamental tasks in guitar tone modeling: EGTT replaces a recording's tone with that of a reference, while EGTR recovers the dry direct-input (DI) signal from a wet, processed recording. Despite their highly related nature, prior work has addressed them independently, and both works have yet to achieve satisfactory results. In this paper, we propose EG-VAE, a unified framework that jointly models EGTT and EGTR by disentangling frame-level content and global tone representations from wet recordings with a variational autoencoder. EGTT is achieved by recombining a source's content with a reference's tone, while EGTR is attained by a novel tone masking objective that enforces content-tone disentanglement during training and realizes the removal procedure at inference. To improve transfer to tones unseen in training, a second training stage shapes a smooth tone space through variational sampling and audio-effects augmentation. Experimental results from both objective and subjective evaluations demonstrate that EG-VAE outperforms task-specific baselines on transfer and removal. Demos are available at https://guitar-tone-demo.vercel.app/.