Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach for transferring the applied effects of a reference audio to an audio track. It optimises the effect parameters to minimise the distance between the style embeddings of the processed audio and the reference. However, this method treats all possible configurations equally and relies solely on the embedding space, which can result in unrealistic configurations or biased outcomes. We address this pitfall by introducing a Gaussian prior derived from the DiffVox vocal preset dataset over the parameter space. The resulting optimisation is equivalent to maximum-a-posteriori estimation. Evaluations on vocal effects transfer on the MedleyDB dataset show significant improvements across metrics compared to baselines, including a blind audio effects estimator, nearest-neighbour approaches, and uncalibrated ST-ITO. The proposed calibration reduces the parameter mean squared error by up to 33% and more closely matches the reference style. Subjective evaluations with 16 participants confirm the superiority of our method in limited data regimes. This work demonstrates how incorporating prior knowledge at inference time enhances audio effects transfer, paving the way for more effective and realistic audio processing systems.

翻译：推理时优化的风格迁移（ST-ITO）是一种将参考音频所施加的效果迁移至目标音频的新近方法。该方法通过优化效果参数，以最小化处理后音频与参考音频在风格嵌入空间中的距离。然而，此方法将所有可能的参数配置视为同等可能，且仅依赖于嵌入空间，这可能导致不现实的参数配置或存在偏差的结果。我们通过引入一个基于DiffVox人声预设数据集、定义在参数空间上的高斯先验来应对此缺陷。由此得到的优化过程等价于最大后验估计。在MedleyDB数据集上进行的人声效果迁移实验表明，相较于基线方法（包括盲音频效果估计器、最近邻方法以及未校准的ST-ITO），所提方法在各项指标上均取得显著提升。所提出的校准方法将参数均方误差降低了最高达33%，并使迁移风格更贴近参考风格。一项包含16名参与者的主观评估进一步证实了在有限数据条件下我们方法的优越性。本工作展示了在推理时融入先验知识如何增强音频效果迁移的效果，为开发更高效、更逼真的音频处理系统铺平了道路。