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 str
The rapid advancement and proliferation of AI music generators necessitate robust detection mechanisms to distinguish between synthetic and human-created content, addressing immediate needs in content authenticity and intellectual property.
This development is crucial for maintaining trust in digital media, protecting creators, and preventing the misuse of AI-generated content in various applications, particularly as AI music becomes more indistinguishable from human work.
The ability to reliably detect AI-generated music, even when it avoids obvious artifacts, changes the landscape for content verification and intellectual property enforcement in the creative industries.
- · Digital audio forensics firms
- · Music industry content ID platforms
- · Original human music creators
- · AI ethicists and regulators
- · Malicious users of AI music generation
- · Platforms without robust content verification
- · Generative AI music models without proper attribution features
Improved detection methods will help combat misattribution and unauthorized use of AI-generated music.
This could lead to new standards and regulations for AI-generated content, impacting how it is produced, distributed, and monetized.
The enhanced ability to differentiate between human and AI content might foster greater appreciation for human artistry, while also pushing AI generators to develop more transparent and auditable methods.
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