A Fourier Explanation of AI-music Artifacts (2506.19108v1)

Abstract: The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringement, job displacement, and ethical implications have led to growing scrutiny and legal challenges. In parallel, AI-detection services have emerged, yet these systems remain largely opaque and privately controlled, mirroring the very issues they aim to address. This paper explores the fundamental properties of synthetic content and how it can be detected. Specifically, we analyze deconvolution modules commonly used in generative models and mathematically prove that their outputs exhibit systematic frequency artifacts -- manifesting as small yet distinctive spectral peaks. This phenomenon, related to the well-known checkerboard artifact, is shown to be inherent to a chosen model architecture rather than a consequence of training data or model weights. We validate our theoretical findings through extensive experiments on open-source models, as well as commercial AI-music generators such as Suno and Udio. We use these insights to propose a simple and interpretable detection criterion for AI-generated music. Despite its simplicity, our method achieves detection accuracy on par with deep learning-based approaches, surpassing 99% accuracy on several scenarios.

• The paper mathematically proves that deconvolution modules inherently generate consistent spectral peaks as fingerprints of AI-generated music.
• The study employs Fourier analysis to reveal that these artifacts originate from the architecture rather than from training data or weights.
• The proposed detection method achieves over 99% accuracy, offering a simpler alternative to complex deep-learning-based approaches.

A Fourier Explanation of AI-Music Artifacts

The paper "A Fourier Explanation of AI-Music Artifacts" by Afchar et al. provides a comprehensive analysis of the frequency-based artifacts inherent in AI-generated music, particularly focusing on those produced by deconvolution modules within generative models. This research offers mathematical proof that certain systematic frequency artifacts manifest as small but distinctive spectral peaks in the outputs of these models, derived from the inherent properties of the chosen architectures rather than the training data or model weights.

Summary

The paper begins by contextualizing the rapid expansion of AI-generated music and the subsequent ethical and legal challenges it faces, such as copyright infringement and job displacement. Despite the popularity of generative AI (GenAI) in music creation, AI-detection services are met with skepticism due to their opaque nature, reminiscent of the generative models they aim to detect.

Central to the research is the mathematical investigation into the deconvolution modules commonly employed in generative models. The authors prove that these modules inherently produce frequency artifacts, akin to the checkerboard artifact known in computer vision. Using Fourier analysis, these artifacts are shown to result from the architecture of the model rather than external training factors. The process involves the periodization of spectra due to deconvolution operations, which creates replicated peaks that can serve as fingerprints for AI-generated content.

Numerical Results and Strong Claims

The paper presents robust experimental validations showing that the phenomenon is architecture-dependent. Various versions of the DAC model, trained on different datasets and with different seeds, consistently exhibit the same artifacts, reinforcing the independence from training data and learned weights. This theoretical insight leads to a practical detection method for AI-music, achieving detection accuracy exceeding 99%, comparable to other advanced deep learning-based approaches.

Implications

The findings have significant implications both theoretically and practically. Theoretically, they contribute to understanding the nature of AI-generated artifacts, providing a foundation upon which further explorations into AI interpretability can be built. Practically, the proposed detection method offers a simpler yet effective alternative to complex deep-learning detectors, potentially aiding in the regulation and monitoring of AI-content creation in the music industry.

Future Directions

Future research can explore the robustness of the proposed method against various audio manipulations and extend the framework to other generative architectures. It is essential for the field to continue developing transparent and explainable AI systems to address rising privacy concerns and ethical implications in AI content creation.

The paper not only demystifies some underlying properties of AI music generation but also proposes an accessible framework for its detection, ensuring a more regulated approach to integrating AI into the music industry.