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

TL;DR

This paper introduces a neural amplifier model capable of emulating multiple guitar amplifiers using tone embeddings, enabling zero-shot generalization to unseen devices through contrastive learning and embedding strategies.

Contribution

It presents a novel one-to-many amplifier modeling approach with contrastive learning for tone embeddings, advancing zero-shot audio device emulation.

Findings

Effective multi-amplifier emulation with a single neural model

Successful zero-shot generalization to unseen amplifiers

Comparison of tone embedding strategies shows improved performance

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.