MusicFlux：面向文本引导音乐生成与编辑的灵活条件控制方法

Authors:Teysir Baoueb (IP Paris, LTCI, IDS, S2A), Leonardo Boulitreau de Menezes Martins Marques (IP Paris, LTCI, IDS, S2A), Paraskevas Stamatiadis (S2A, LTCI, IDS), Ga{ë}l Richard (S2A, IDS, IP Paris, LTCI)

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Abstract:Music editing transforms existing recordings while preserving their original attributes. Recent approaches adapt pretrained text-to-music models for tasks like inpainting, event insertion, or restoration by conditioning them with context audio. It was recently proposed for image editing to unify generation and editing by treating the source image as in-context information. However, applying this directly to music is computationally expensive due to long audio sequences. In this work, we develop MusicFlux, a text-guided music editor built on an efficient pretrained flow-matching text-to-audio model. We introduce two conditioning approaches that trade off editing performance and computational efficiency. Experiments show that both are competitive with strong baselines, with one achieving better editing quality and the other preserving the original sequence length and substantially improving computational efficiency.

Comments:	Submitted to ICASSP 2027
Subjects:	Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as:	arXiv:2611.13834 [cs.SD]
	(or arXiv:2611.13834v1 [cs.SD] for this version)
	https://doi.org/10.48550/arXiv.2611.13834 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Teysir Baoueb [view email] [via CCSD proxy]
[v1] Tue, 10 Nov 2026 10:00:03 UTC (176 KB)