Synth-JEPA：用于无渲染合成器参数搜索的联合嵌入预测

中文摘要 AI 辅助

声音匹配可以被形式化为针对音频域目标优化合成器参数的问题。然而，从通用音频表示中导出的目标往往难以优化，而直接搜索则需要渲染每个候选参数。我们引入了Synth-JEPA，它从配对的合成器数据中学习相互预测的音频和参数表示。在推理时，候选参数直接在这个学习到的空间中评分，从而产生一个无渲染目标，其音频几何形状由参数对应关系而非通用音频相似性塑造。我们在Surge XT上使用保留的合成器声音和域外的NSynth和FSD50K目标评估了Synth-JEPA，并与逆模型、直接搜索和学习到的代理目标进行了比较。Synth-JEPA在域内优于所有基线，并在域外保持竞争力。其匹配质量随着额外的测试时搜索而持续提高，允许用计算量换取匹配质量。在成对听力测试中，听众在总体85%的试验中偏好Synth-JEPA。这些结果共同表明，具有参数诱导几何形状的音频表示使得合成器声音匹配可以被视为一个有效的无渲染搜索问题。

英文摘要

Sound matching can be formulated as optimizing synthesizer parameters against an audio-domain objective. However, objectives derived from generic audio representations are often difficult to optimize, while direct search requires rendering every candidate. We introduce Synth-JEPA, which learns mutually predictive audio and parameter representations from paired synthesizer data. At inference, candidate parameters are scored directly in this learned space, yielding a renderer-free objective whose audio geometry is shaped by parameter correspondences rather than generic audio similarity. We evaluate Synth-JEPA on Surge XT using held-out synthesizer sounds and out-of-domain NSynth and FSD50K targets, against inverse models, direct search, and learned proxy objectives. Synth-JEPA outperforms all baselines in-domain and remains competitive out-of-domain. Its matching quality continues to improve with additional test-time search, allowing compute to be traded for match quality. In pairwise listening tests, listeners preferred Synth-JEPA in 85% of trials overall. Together, these results show that an audio representation with a parameter-induced geometry allows synthesizer sound matching to be approached as an effective renderer-free search problem.

发表机构

• Sony Computer Science Laboratories（索尼计算机科学实验室）
• Queen Mary University of London（伦敦玛丽女王大学）

机构由 AI 辅助整理，请以论文原文为准。