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Papers

arxiv:2402.13763

Music Style Transfer with Time-Varying Inversion of Diffusion Models

Published on Feb 21, 2024

· Submitted by

AK on Feb 22, 2024

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Authors:

Yuxin Zhang ,

Abstract

A method for music style transfer captures musical attributes using minimal data with a novel time-varying textual inversion module and bias-reduced stylization technique.

AI-generated summary

With the development of diffusion models, text-guided image style transfer has demonstrated high-quality controllable synthesis results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the limited availability of matched audio-text datasets. Music, being an abstract and complex art form, exhibits variations and intricacies even within the same genre, thereby making accurate textual descriptions challenging. This paper presents a music style transfer approach that effectively captures musical attributes using minimal data. We introduce a novel time-varying textual inversion module to precisely capture mel-spectrogram features at different levels. During inference, we propose a bias-reduced stylization technique to obtain stable results. Experimental results demonstrate that our method can transfer the style of specific instruments, as well as incorporate natural sounds to compose melodies. Samples and source code are available at https://lsfhuihuiff.github.io/MusicTI/.

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