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

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-...

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Bibliographic Details
Published in:arXiv.org 2024-07
Main Authors: Yu-Hua, Chen, Yen-Tung Yeh, Yuan-Chiao, Cheng, Wu, Jui-Te, Yu-Hsiang, Ho, Jang, Jyh-Shing Roger, Yang, Yi-Hsuan
Format: Article
Language:English
Subjects:
Amplifiers
Analog circuits
Embedding
Modelling
Representations
Online Access:Get full text
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Summary: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.
ISSN:2331-8422