AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing

• URL: http://arxiv.org/abs/2605.31053v1
• Date: Fri, 29 May 2026 09:25:55 GMT
• Title: AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing
• Abstract summary: AnchorSteer is a framework that disentangles tension by coupling structural anchoring with self-discovered semantic steering.<n>The proposed approach probes internal representations to extract interpretable, label-free concept vectors.<n>Experiments on ZoME-Bench and subjective tests show that the proposed framework outperforms both steering-only and anchoring-only baselines.
• Score: 14.255074251309168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controllable music editing is to modify high-level attributes while strictly preserving rhythmic and melodic structures. However, this task is challenged by a semantic-structural entanglement: steering methods often degrade structure to achieve editing performance, while structural adaptors suppress semantic responsiveness. We propose AnchorSteer, a framework that disentangles this tension by coupling structural anchoring with self-discovered semantic steering. The proposed approach probes internal representations to extract interpretable, label-free concept vectors via a self-supervised reconstruction objective, isolating attributes without curated data. During editing, these portable, plug-and-play concept vectors are injected into diffusion hidden manifolds while a structural adaptor enforces consistency. Variants for unconditioned and conditioned injections are provided to balance robustness and semantic strength. Experiments on ZoME-Bench and subjective tests show that the proposed framework outperforms both steering-only and anchoring-only baselines, enabling significant semantic transformations with high-fidelity structural preservation.

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