aiqbench analysis
This paper introduces MuseCPEval, the first comprehensive framework designed to evaluate how well music editing systems preserve essential musical attributes like timbre and genre during tasks such as instrument substitution. By establishing fine-grained metrics validated through both objective analysis and human studies, it addresses a critical gap in current research where context preservation is often overlooked. This work provides a necessary diagnostic tool for developing more reliable AI-driven audio synthesis and editing pipelines.
Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems
arXiv:2512.14629v2 Announce Type: replace-cross 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
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
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2512.14629 [cs.SD]
(or arXiv:2512.14629v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2512.14629 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 269 words
