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

arXiv: https://arxiv.org/abs/2512.14629 · HTML: https://mlsanity.com/paper/2512.14629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion](https://mlsanity.com/paper/2402.10009/) (2024)
- [Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning](https://mlsanity.com/paper/2405.18386/) (2024)
- [High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching](https://mlsanity.com/paper/2407.03648/) (2024)
- [Editing Music with Melody and Text: Using ControlNet for Diffusion Transformer](https://mlsanity.com/paper/2410.05151/) (2024)
- [Audio Prompt Adapter: Unleashing Music Editing Abilities for Text-to-Music with Lightweight Finetuning](https://mlsanity.com/paper/2407.16564/) (2024)
- [WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning](https://mlsanity.com/paper/2509.04744/) (2025)
- [SteerMusic: Enhanced Musical Consistency for Zero-shot Text-guided and Personalized Music Editing](https://mlsanity.com/paper/2504.10826/) (2025)
- [A Survey on Evaluation Metrics for Music Generation](https://mlsanity.com/paper/2509.00051/) (2025)
- [Melodia: Training-Free Music Editing Guided by Attention Probing in Diffusion Models](https://mlsanity.com/paper/2511.08252/) (2025)
- [Music Proofreading with RefinPaint: Where and How to Modify Compositions given Context](https://mlsanity.com/paper/2407.09099/) (2024)
- [Not that Groove: Zero-Shot Symbolic Music Editing](https://mlsanity.com/paper/2505.08203/) (2025)

## Built on by (most cited first, 1 of 1)

- [World Models Under Asynchronous Sensor Observations](https://mlsanity.com/paper/2609.07299/) (2026)

## Abstract (arXiv)

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