CLEF: Learning Closed-Loop Control for Text-Guided Music Editing

In this paper, we propose CLEF, a framework that learns closed-loop control for text-guided music editing. Our key idea is to use the outcome of each editing round to adjust the guidance for the next, preserving the source audio’s structure while faithfully realizing the requested edits. Specifically, we construct two complementary feedback signals to assess unintended structural changes and insufficient adherence to the editing instruction. Temporal structural feedback uses our proposed TACons metric to evaluate how well the source audio’s temporal structure is preserved. Target semantic feedback measures alignment between the edited audio and the target text using CLAP, assessing whether the requested attributes have been faithfully realized. To translate these evaluations into control over subsequent edits, we learn a feedback-aware Compensation Controller that combines both feedback signals with the current editing state to predict the direction and magnitude of updates to the target textual conditioning representations. At inference, the updated conditioning guides the next editing round, whose output is evaluated again to inform subsequent compensation. This closes the feedback loop and allows the same trained controller to refine the conditioning over multiple rounds, adapting the editing process to the observed structural and semantic discrepancies. Moreover, CLEF can flexibly incorporate an additional reference feedback signal to assess attribute consistency with reference audio, further enhancing personalized editing. Extensive experiments demonstrate that CLEF consistently outperforms state-of-the-art baselines, achieving a 23.78% improvement in CQT1-PCC and a 4.18% gain in CLAP, along with superior subjective listening quality.