Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911955773554688 |
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| author | Chen, Yu-Hua Yeh, Yen-Tung Cheng, Yuan-Chiao Wu, Jui-Te Ho, Yu-Hsiang Jang, Jyh-Shing Roger Yang, Yi-Hsuan |
| author_facet | Chen, Yu-Hua Yeh, Yen-Tung Cheng, Yuan-Chiao Wu, Jui-Te Ho, Yu-Hsiang Jang, Jyh-Shing Roger Yang, Yi-Hsuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10646 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control Chen, Yu-Hua Yeh, Yen-Tung Cheng, Yuan-Chiao Wu, Jui-Te Ho, Yu-Hsiang Jang, Jyh-Shing Roger Yang, Yi-Hsuan Sound Audio and Speech Processing 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. |
| title | Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.10646 |