Abstract
Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions. This paper introduces a novel training-free approach leveraging pre-trained Latent Diffusion Models (LDMs). By manipulating the self-attention features of the LDM, we effectively transfer the style of reference music onto content music without additional training. Our method achieves superior style transfer and melody preservation compared to existing methods. This work opens new creative avenues for personalized music generation.
Abstract (translated)
音乐风格转换虽然为个性化音乐生成提供了令人兴奋的可能性，但通常需要大量的训练或详细的文本描述。本文介绍了一种新颖的无需训练的方法，利用预训练的潜在扩散模型（LDM）。通过操纵LDM的自注意力特征，我们能够在不需要额外训练的情况下将参考音乐的风格转移到内容音乐上。与现有方法相比，我们的方法在风格转换和旋律保真方面表现更优。这项工作为个性化音乐生成开辟了新的创意途径。
URL
https://arxiv.org/abs/2411.15913
