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This research addresses a critical challenge in AI audio by proposing Sofia, a framework capable of detecting synthetic songs using music-intrinsic features rather than relying on transient generation artifacts. By leveraging an adaptive Mixture-of-Experts module and introducing the MUSIC8K benchmark, the study demonstrates significant improvements in F1 scores across diverse emerging generators while maintaining robustness against realistic perturbations.
Beyond Artifacts: Towards Generalizable Synthetic Song Detection via Music-Intrinsic Features
arXiv:2606.16612v1 Announce Type: cross Abstract: The rapid advancement of AI music generators highlights the urgent need for reliable Synthetic Song Detection (SSD). Existing SSD methods often rely on low-level artifacts or fixed feature assumptions, struggling to capture generator-agnostic cues. To address this, we propose Sofia (Synthetic-song detection framework via music features), a flexible framework that models music-intrinsic attributes via feature-specific experts and an adaptive Mixture-of-Experts (MoE) module. By configuring Sofia with representative Vocal, Audio-effect, Global structure features, and their combinations, we present their individual and complementary contributions. To comprehensively evaluate our framework, we further construct MUSIC8K, a challenging benchmark featuring lastest emerging generators and realistic audio perturbations. Experiments show that Sofia learns generator-agnostic representations from music-intrinsic features, improving the F1 score by 18.5 points over the strongest baseline on MUSIC8K-O while maintaining strong robustness.
Abstract:The rapid advancement of AI music generators highlights the urgent need for reliable Synthetic Song Detection (SSD). Existing SSD methods often rely on low-level artifacts or fixed feature assumptions, struggling to capture generator-agnostic cues. To address this, we propose Sofia (Synthetic-song detection framework via music features), a flexible framework that models music-intrinsic attributes via feature-specific experts and an adaptive Mixture-of-Experts (MoE) module. By configuring Sofia with representative Vocal, Audio-effect, Global structure features, and their combinations, we present their individual and complementary contributions. To comprehensively evaluate our framework, we further construct MUSIC8K, a challenging benchmark featuring lastest emerging generators and realistic audio perturbations. Experiments show that Sofia learns generator-agnostic representations from music-intrinsic features, improving the F1 score by 18.5 points over the strongest baseline on MUSIC8K-O while maintaining strong robustness.
Sound (cs.SD); Machine Learning (cs.LG); Multimedia (cs.MM)
arXiv:2606.16612 [cs.SD]
(or arXiv:2606.16612v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2606.16612 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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