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Music Style Transfer with Time-Varying Inversion of Diffusion Models (2402.13763v1)

Published 21 Feb 2024 in cs.SD and eess.AS

Abstract: 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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Citations (9)
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Summary

  • The paper introduces a groundbreaking time-varying textual inversion module that captures dynamic style features for effective music style transfer.
  • It employs an example-based approach that preserves melody and rhythm while integrating diverse stylistic elements from various audio sources.
  • Experimental results demonstrate superior qualitative and quantitative performance, highlighting its potential to advance creative music generation.

Time-Varying Textual Inversion for Music Style Transfer: A Novel Approach

Introduction to Music Style Transfer and Its Challenges

Music style transfer is a fascinating area of research that intersects music, technology, and creativity. The overarching goal is to transfer the stylistic elements of one piece of audio (the style) to another (the content), without altering the inherent content of the latter. While there has been significant progress in the domain of music style transfer, particularly through deep learning technologies, the field faces unique challenges. One of the key hurdles is the intricate and abstract nature of music itself, which often eludes precise textual or even musical description, especially for non-standard instruments or natural and synthesized sounds.

Novel Contributions of the Paper

This paper introduces a groundbreaking method that addresses the nuanced challenge of music style transfer with minimal reliance on large datasets or precise textual descriptions. Central to the paper's contribution is the introduction of a time-varying textual inversion module, which ingeniously captures the textural and structural features of music in a style representation, enabling effective style transfer across a wide spectrum of musical and non-musical sounds.

The principal contributions are threefold:

  • The introduction of an example-based method for music style transfer that is adept at handling instruments, natural sounds, and synthesized effects with high creativity.
  • The novel application of time-varying textual inversion, which dynamically captures the mel-spectrogram features at various levels, ensuring a precise style representation.
  • Experimental validation that demonstrates superior performance in both qualitative and quantitative metrics, particularly in achieving style transfer from minimal examples and preserving content integrity seamlessly.

Time-Varying Textual Inversion: The Core Innovation

At the heart of this paper's methodology is the time-varying textual inversion module. This module allows for the dynamic representation of various musical styles, including those produced by niche instruments or encompassing complex natural sounds, by embedding an audio clip's style into a latent space through a pseudo-word representation. This process leverages diffusion models and modifies text embeddings across different timesteps, ensuring a nuanced capture of both textural and structural elements of the music. The method's effectiveness is showcased through its ability to maintain the content's melody and rhythm while introducing stylistic alterations from the target audio.

Experimental Findings and Implications

The experimental results serve as a robust testament to the method's efficacy, demonstrating its superiority in content preservation and style fit over existing techniques. Through qualitative and quantitative evaluations, the method has shown remarkable results in transferring styles from specific instruments and incorporating natural sounds into compositions, broadening the horizons for creative music generation. Furthermore, the approach's efficiency in using a small dataset and achieving high levels of musicality even with non-musical style audio underscores its potential in enhancing musical creativity and accessibility.

Looking Forward: Potential Avenues for Future Research

While the achievements of this paper are significant, it also opens up various avenues for future research. There's a compelling interest in exploring more interpretable and attribute-disentangled methods for music style transfer, enhancing the ability to manipulate specific musical characteristics explicitly. Additionally, the incorporation of stronger generative models may further refine style representation and transfer capabilities, promising even more accurate and creative outputs in music generation tasks.

Acknowledgments

The paper acknowledges the support of the National Natural Science Foundation of China, highlighting the collaborative effort behind this innovative research.

Conclusion

In summary, this paper marks a significant advancement in the music style transfer domain, introducing a highly effective and novel methodological approach. By leveraging time-varying textual inversion and minimal data, it achieves remarkable results in transferring musical styles across a range of audio types. This work not only sets a new benchmark for music style transfer tasks but also opens up exciting possibilities for future innovations in artificial intelligence-driven music generation.

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