不用梯度，用搜索找到最符合文字描述的音频效果组合。
FxSearcher: gradient-free text-driven audio transformation
- 用贝叶斯优化+文本评分器自动找最佳音频处理参数
- 在多个指标上得分媲美人类偏好，最高达9.2分（满分10）
- 适合需要精准控制音效风格的音乐创作和影视后期
从文本提示实现多样且高质量的音频变换仍具挑战性，因现有方法严重依赖有限的可微分音频效果。本文提出FxSearcher，一种新型无梯度框架，通过贝叶斯优化与基于CLAP的评分函数，高效搜索满足文本提示的最优音频效果配置。引入引导提示以避免不良失真并提升人听偏好。为客观评估，我们构建了基于AI的评价体系。结果表明，该方法在各项指标上的最高得分与人类偏好高度一致，最高达9.2分（满分10）。演示视频见https://hojoonki.github.io/FxSearcher/
原文摘要 · Abstract (English)
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/
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