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This paper introduces EG-VAE, a unified framework that simultaneously addresses electric guitar tone transfer (EGTT) and tone removal (EGTR) by employing a variational autoencoder to disentangle frame-level content and global tone representations. The method enhances performance by incorporating a novel tone masking objective and a second training stage that utilizes variational sampling and audio-effects augmentation to create a smooth tone space. Experimental evaluations show that EG-VAE significantly outperforms existing task-specific models in both tone transfer and removal tasks.
A novel unified framework for electric guitar tone transfer and removal achieves superior performance by disentangling content and tone representations, reshaping how we approach guitar tone modeling.
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/.
