{"ID":2900944,"CreatedAt":"2026-06-01T05:51:17.9442275Z","UpdatedAt":"2026-06-01T06:23:29.641557848Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2605.31053","arxiv_id":"2605.31053","title":"AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing","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.","short_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 responsive...","url_abs":"https://arxiv.org/abs/2605.31053","url_pdf":"https://arxiv.org/pdf/2605.31053v1","authors":"[\"Chih-Heng Chang\",\"Keng-Seng Ho\",\"Chih-Yu Tsai\",\"Kuan-Lin Chen\",\"Yi-Hsuan Yang\",\"Jian-Jiun Ding\"]","published":"2026-05-29T09:25:55Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\"]","methods":"[\"Diffusion Model\"]","has_code":false}