Music Style Transfer with Time-Varying Inversion of Diffusion Models
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/.
65d757049103d35b77b0b8ea
Sifei Li
65d757049103d35b77b0b8eb
Yuxin Zhang
claimed_verified
2025-06-05T12:42:51.908Z
63228c7c15b7beab57c9ab0b
https://cdn-avatars.huggingface.co/v1/production/uploads/1663208563439-noauth.jpeg
Yuxin Zhang
user
ZHazel
65d757049103d35b77b0b8ec
Fan Tang
65d757049103d35b77b0b8ed
Chongyang Ma
65d757049103d35b77b0b8ee
Weiming dong
65d757049103d35b77b0b8ef
Changsheng Xu
Music Style Transfer with Time-Varying Inversion of Diffusion Models
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/.
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.
diffusion models music style transfer mel-spectrogram features time-varying textual inversion bias-reduced stylization