AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913172359741440 |
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| author | Chang, Chih-Heng Ho, Keng-Seng Tsai, Chih-Yu Chen, Kuan-Lin Yang, Yi-Hsuan Ding, Jian-Jiun |
| author_facet | Chang, Chih-Heng Ho, Keng-Seng Tsai, Chih-Yu Chen, Kuan-Lin Yang, Yi-Hsuan Ding, Jian-Jiun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31053 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing Chang, Chih-Heng Ho, Keng-Seng Tsai, Chih-Yu Chen, Kuan-Lin Yang, Yi-Hsuan Ding, Jian-Jiun Sound Artificial Intelligence 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. |
| title | AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2605.31053 |