In this work, we investigate the personalization of text-to-music diffusion
models in a few-shot setting. Motivated by recent advances in the computer
vision domain, we are the first to explore the combination of pre-trained
text-to-audio diffusers with two established personalization methods. We
experiment with the effect of audio-specific data augmentation on the overall
system performance and assess different training strategies. For evaluation, we
construct a novel dataset with prompts and music clips. We consider both
embedding-based and music-specific metrics for quantitative evaluation, as well
as a user study for qualitative evaluation. Our analysis shows that similarity
metrics are in accordance with user preferences and that current
personalization approaches tend to learn rhythmic music constructs more easily
than melody. The code, dataset, and example material of this study are open to
the research community.

调查了在 few-shot 设置中对文本到音乐扩散模型进行个性化的工作。首次探索了将预训练的文本到音频扩散器与两种常用的个性化方法结合的组合。实验了音频特定数据增强对系统整体性能的影响，并评估了不同的训练策略。构建了一个包含提示和音乐片段的新数据集进行评估，使用基于嵌入和基于音乐的量化评估指标，以及用户研究进行定性评估。分析表明相似度度量与用户偏好一致，并且当前的个性化方法更容易学习到节奏音乐结构而非旋律。该研究的代码、数据集和示例资料对研究社区开放。