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arXiv · 2407.10646

Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control

Yu-Hua Chen·Yen-Tung Yeh·Yuan-Chiao Cheng·Jui-Te Wu·Yu-Hsiang Ho·Jyh-Shing Roger Jang·Yi-Hsuan Yang
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Abstract

Replicating analog device circuits through neural audio effect modeling has garnered increasing interest in recent years. Existing work has predominantly focused on a one-to-one emulation strategy, modeling specific devices individually. In this paper, we tackle the less-explored scenario of one-to-many emulation, utilizing conditioning mechanisms to emulate multiple guitar amplifiers through a single neural model. For condition representation, we use contrastive learning to build a tone embedding encoder that extracts style-related features of various amplifiers, leveraging a dataset of comprehensive amplifier settings. Targeting zero-shot application scenarios, we also examine various strategies for tone embedding representation, evaluating referenced tone embedding against two retrieval-based embedding methods for amplifiers unseen in the training time. Our findings showcase the efficacy and potential of the proposed methods in achieving versatile one-to-many amplifier modeling, contributing a foundational step towards zero-shot audio modeling applications.

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Yu-Hua Chen, Yen-Tung Yeh, Yuan-Chiao Cheng, Jui-Te Wu, Yu-Hsiang Ho, Jyh-Shing Roger Jang, Yi-Hsuan Yang. 2024-07-15. Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control. https://arxiv.org/abs/2407.10646

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