484 - SteerMusic: Enhanced Musical Consistency for Zero-shot Text-Guided and Personalized Music Editing

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Authors: Association for Artificial Intelligence 2026, Kin Cheuk, Woosung Choi, Giorgio Fabbro, Chieh-Hsin Lai, Wei‐Hsiang Liao, Michele Mancusi, Charles Martin, Yuki Mitsufuji, Naoki Murata, Xinlei Niu, Jing Zhang
Cited by: 0

Abstract

Music editing is an important step in music production, which has broad applications, including game development and film production. Most existing zero-shot text-guided editing methods rely on pretrained diffusion models by involving forward-backward diffusion processes. However, these methods often struggle to preserve the musical content. Additionally, text instructions alone usually fail to accurately describe the desired music. In this paper, we propose two music editing methods that improve the consistency between the original and edited music by leveraging score distillation. The first method, SteerMusic, is a coarse-grained zero-shot editing approach using delta denoising score. The second method, SteerMusic+, enables fine-grained personalized music editing by manipulating a concept token that represents a user-defined musical style. SteerMusic+ allows for the editing of music into user-defined musical styles that cannot be achieved by the text instructions alone. Experimental results show that our methods outperform existing approaches in preserving both music content consistency and editing fidelity. User studies further validate that our methods achieve superior music editing quality.

Fields of Study & Subjects

Music and Audio Processing Generative Adversarial Networks and Image Synthesis Music Technology and Sound Studies

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN