Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems

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arXiv cs.AI · Yash Vishe, Eric Xue, Xunyi Jiang, Zachary Novack, Junda Wu, Julian McAuley, Xin Xu · 2026-08-20 AI

[Submitted on 16 Dec 2025 (v1), last revised 18 Aug 2026 (this version, v2)]

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Abstract:Music editing plays a vital role in modern music production, with applications in film, broadcasting, and game development. Recent advances in music editing systems have enabled diverse editing tasks such as timbre transfer, instrument substitution, and genre transformation. However, many existing works overlook evaluating their ability to preserve musical facets that should remain unchanged during editing, which we define as Music Context Preservation (MuseCP). While some studies do consider MuseCP, their evaluation protocols and metrics are not comprehensive. To address this, we introduce the first MuseCP evaluation framework, MuseCPEval, that covers four categories of music facets with fine-grained and well-tailored metrics to capture nuanced changes in music attributes. Objective validation and a human study demonstrate the effectiveness of these metrics. Moreover, the case studies on diverse music editing systems illustrate the practical utility of these metrics as a testbed and diagnostic tool, providing insights into the strengths and limitations of existing systems. We hope our metrics and findings can offer practical guidance for developing more effective and reliable music editing strategies with strong MuseCP capability

Submission history

From: Yash Vishe [view email]
[v1] Tue, 16 Dec 2025 17:44:56 UTC (820 KB)
[v2] Tue, 18 Aug 2026 22:10:36 UTC (389 KB)

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추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2512.14629