Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control
<|reference_start|>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-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.<|reference_end|>
@article{chen2024towards,
    title={Towards zero-shot amplifier modeling: One-to-many amplifier modeling via
  tone embedding control},
    author={Yu-Hua Chen, Yen-Tung Yeh, Yuan-Chiao Cheng, Jui-Te Wu, Yu-Hsiang Ho,
  Jyh-Shing Roger Jang, and Yi-Hsuan Yang},
    journal={arXiv preprint arXiv:2407.10646},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10646},
    primaryClass={cs.SD eess.AS}
}