  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.
