FxSearcher: gradient-free text-driven audio transformation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912720231596032 |
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| author | Ki, Hojoon Kim, Jongsuk Kwon, Minchan Kim, Junmo |
| author_facet | Ki, Hojoon Kim, Jongsuk Kwon, Minchan Kim, Junmo |
| contents | Achieving diverse and high-quality audio transformations from text prompts remains challenging, as existing methods are fundamentally constrained by their reliance on a limited set of differentiable audio effects. This paper proposes FxSearcher, a novel gradient-free framework that discovers the optimal configuration of audio effects (FX) to transform a source signal according to a text prompt. Our method employs Bayesian Optimization and CLAP-based score function to perform this search efficiently. Furthermore, a guiding prompt is introduced to prevent undesirable artifacts and enhance human preference. To objectively evaluate our method, we propose an AI-based evaluation framework. The results demonstrate that the highest scores achieved by our method on these metrics align closely with human preferences. Demos are available at https://hojoonki.github.io/FxSearcher/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FxSearcher: gradient-free text-driven audio transformation Ki, Hojoon Kim, Jongsuk Kwon, Minchan Kim, Junmo Audio and Speech Processing Sound Achieving diverse and high-quality audio transformations from text prompts remains challenging, as existing methods are fundamentally constrained by their reliance on a limited set of differentiable audio effects. This paper proposes FxSearcher, a novel gradient-free framework that discovers the optimal configuration of audio effects (FX) to transform a source signal according to a text prompt. Our method employs Bayesian Optimization and CLAP-based score function to perform this search efficiently. Furthermore, a guiding prompt is introduced to prevent undesirable artifacts and enhance human preference. To objectively evaluate our method, we propose an AI-based evaluation framework. The results demonstrate that the highest scores achieved by our method on these metrics align closely with human preferences. Demos are available at https://hojoonki.github.io/FxSearcher/ |
| title | FxSearcher: gradient-free text-driven audio transformation |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2511.14138 |