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

Yash Vishe, Eric Xue, Xunyi Jiang, Zachary Novack, Junda Wu, Julian McAuley, Xin Xu

arXiv:2512.14629v2cs.SDcs.AI

TL;DR

Existing music-editing evaluations often provide limited and non-comprehensive evidence about preserving musical attributes that should remain unchanged. This paper introduces MuseCPEval, a four-facet framework with ten tailored metrics, and validates it objectively, through human listening, and across diverse systems. The results support its effectiveness and practical value as a testbed and diagnostic tool for analyzing MuseCP.

  • Problem

    Existing music-editing evaluations incompletely assess preservation of non-target musical attributes, despite the importance of retaining facets such as harmony, rhythm, and structure.

  • Method

    MuseCPEval evaluates similarity between reference and edited music across harmony, rhythm & meter, structure, and melody & motifs using fine-grained metrics.

  • Results

    Objective validation and human listening studies show that MuseCPEval metrics capture nuanced musical-attribute changes and reflect perceptually meaningful differences.

  • Takeaways & Limitations

    Case studies show that MuseCPEval provides practical testbed and diagnostic insights into the strengths and limitations of diverse music-editing systems.

Abstract

from arXiv · show

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

1. INTRODUCTION

Existing music-editing evaluations often incompletely assess whether non-target musical attributes remain preserved. MuseCPEval addresses this gap with a comprehensive framework spanning four facets and fine-grained metrics, validated objectively and through human studies and applied diagnostically to diverse systems.

  • Motivation: MuseCP measures whether a music-editing system retains source attributes that are not intended to be edited.These attributes include fundamental facets such as harmony, rhythm, and structure.
  • Motivation: Existing music-editing works evaluate MuseCP incompletely, with some assessing only structure, harmony, or melody and others omitting it entirely.This limits understanding of systems’ strengths and weaknesses.
  • MuseCPEval: MuseCPEval is introduced as the first comprehensive MuseCP framework, covering four music facets with tailored fine-grained metrics.The framework reorganizes preserved attributes into harmony, rhythm & meter, structure, and melody & motif.
  • Validation: The metrics are validated through objective experiments and human listening studies to assess nuanced musical-attribute changes and perceptual alignment.The validation demonstrates metric effectiveness and strong alignment between metric values and human perception for musical differences.
  • Case studies: Case studies apply MuseCPEval to four representative systems spanning diverse methodologies, architectures, and editing tasks.The analyses examine strengths and limitations in preserving different music attributes.
  • Case studies: The framework functions as a practical testbed and diagnostic tool for understanding existing systems and guiding more reliable music-editing development.Its detailed analyses provide insights that conventional evaluations may miss.

2. MUSECPEVAL

MuseCPEval evaluates whether non-target musical attributes remain preserved after editing through four facets and fine-grained metrics. Its measures cover harmony, rhythm and meter, structure, and melody and motif at complementary levels of granularity.

  • Framework overview: MuseCPEval measures preservation between original music x and edited output x′ across harmony, rhythm and meter, structure, and melody and motifs.The framework provides a multi-facet view of non-target attribute preservation.
  • Harmony: Harmony metrics compare tonal keys, chroma distributions, and temporally aligned pitch-class content between x and x′.They include Circle-of-fifths Distance, Chroma Similarity, and Chroma DTW Similarity.
  • Rhythm & Meter: Rhythm and meter metrics assess global tempo difference, beat alignment, and consistency of beat-phase timing errors.The framework uses Folded Beat Difference, Beat F-measure, and Information Gain at different granularities.
  • Structure: Structural preservation is measured by pairwise segment-label agreement and chance-corrected agreement across time-point pairs.Structural Pairwise F-measure evaluates shared grouping, while Adjusted Rand Index also rewards agreement on separation and discounts chance matches.
  • Melody & Motif: Melody and motif preservation are evaluated through tempo-elastic pitch-class trajectory similarity and recovery of length-three interval patterns.Contour DTW Similarity captures aligned pitch-class trajectories, while Motif 3-gram Recall measures how many reference motifs reappear in the edit.
  • Validation: Objective validation shows that most metrics respond consistently to facet-targeted edits, including harmonic sensitivity to pitch shifting and rhythmic sensitivity to tempo scaling.The validation uses 50 MIDI samples, eight tailored operations covering four facets, audio transfer, and mean and standard-deviation reporting.

3. METRIC VALIDATION

The proposed MuseCPEval metrics are validated through objective edits with known facet effects and a human listening study. Results show sensitivity to nuanced attribute changes and generally strong alignment with gold labels and human perception, with melody & motif proving more subjective.

  • 3.1 Objective Validation: Objective validation applies eight tailored editing operations to 50 Lakh MIDI samples spanning four music facets, comparing measured values with expected outcomes.The edited samples are transferred to audio, and metrics are evaluated using means and standard deviations across samples.
  • 3.1 Objective Validation: Pitch-shifting changes harmonic metrics while leaving rhythm metrics unaffected, whereas +50% tempo scaling produces large ∆BPM and small BeatF and IG.These contrasting responses demonstrate facet-specific sensitivity to known musical edits.
  • 3.1 Objective Validation: Structural metrics can fall below 1.0 for structure-preserving edits because segmentation responds to spectral changes, but they still distinguish structural from structure-preserving edits.StructPairF and ARI therefore function as relative indicators rather than absolute measures in these cases.
  • 3.2 Human Listening Study: The human study compares edited samples with different editing strengths against originals, asking listeners which sample deviates farther on a specified facet.This complements objective validation because metric behavior alone may not guarantee alignment with human perception.
  • 3.2 Human Listening Study: Metric–Gold agreement is high across all facets, while Metric–Human agreement is reasonably aligned for most facets but lower for melody & motif.The results support the metrics’ reliability and perceptual relevance, while indicating that melody & motif is harder to quantify.

4. CASE STUDIES: MUSIC EDITING SYSTEM ANALYSIS THROUGH MUSECP

MuseCPEval analyzes four diverse music editing systems using their respective evaluation setups, revealing facet-specific preservation strengths and limitations. Because the setups differ, the systems are not directly comparable, but the framework provides fine-grained diagnostic insight beyond aggregate quality scores.

  • 4. CASE STUDIES: MUSIC EDITING SYSTEM ANALYSIS THROUGH MUSECP: The case studies apply MuseCPEval to MusicMagus, ZETA, Audio Prompt Adapter, and Instruct-MusicGen across different architectures, editing paradigms, and tasks.The analysis is intended to expose preservation strengths and limitations that conventional aggregate evaluations miss.
  • 4.1 Setups: MusicMagus uses text-guided latent embedding steering with cross-attention consistency constraints for zero-shot instrument and style transfer.Its evaluation follows the official protocol with 60 AudioLDM2-generated samples and paired editing instructions.
  • 4.1 Setups: ZETA inverts source music into a pretrained diffusion model’s latent trajectory and uses target-text-conditioned reverse denoising to generate edits while preserving coarse structure.The study evaluates 324 outputs generated from 34 MedleyDB excerpts and 324 editing instructions.
  • 4.1 Setups: Audio Prompt Adapter injects AudioMAE features through decoupled cross-attention in AudioLDM2 for text-guided timbre transfer.The evaluation uses 40 in-domain samples with three instrument-editing instructions per sample.
  • 4.1 Setups: Instruct-MusicGen extends MusicGen with audio and text fusion modules for instrument-level adding, removing, and extracting tasks.The setup evaluates 150 input audio and instruction pairs for each edit.
  • 4.2 Case Analysis: MusicMagus preserves harmony and structural form strongly, retains melodic contour relatively well, and shows low ∆BPM, while near-zero BeatF and IG may reflect meaningful rhythmic style transformations.The results associate its preservation profile with cross-attention consistency and inference-time intervention without additional fine-tuning.
  • 4.2 Case Analysis: ZETA shows particularly strong harmony and beat preservation, but stronger prompt-driven edits may alter higher-level organization or melodic details.Its results suggest suitability for edits that preserve harmonic progression and rhythmic groove.
  • 4.2 Case Analysis: Audio Prompt Adapter preserves harmony and structure strongly, including 0.95 ChromaSim and 0.88 StructPairF, but has weaker beat and melody preservation with large ∆BPM.The results suggest high-level audio embeddings constrain global context more effectively than fine-grained temporal alignment and melodic trajectories.

5. CONCLUSION

MuseCPEval is a comprehensive benchmark for measuring whether music editing systems preserve unintended-to-change musical attributes. It combines ten metrics across four facets with validation studies and case studies to provide practical evaluation insights.

  • MuseCPEval measures whether music editing systems preserve musical attributes that are not intended to change during editing.
  • Ten tailored metrics cover four music facets for comprehensive Music Context Preservation evaluation.
  • Objective validation and a human listening study show that the metrics capture fine-grained changes in music attributes.
  • Case studies on four representative music editing systems provide practical insights into their strengths and weaknesses.

6. AI USAGE AND ETHICS STATEMENT

The authors report limited ChatGPT use for language polishing and describe human-led technical development. The study involved voluntary anonymous participation, compensation, no sensitive personal data, and public release of data and code.

  • ChatGPT was used only for limited language polishing; human authors developed the technical content, experiments, implementation, results, and conclusions.
  • The human listening study was voluntary and anonymous, collected no sensitive personal data, and compensated participants fairly.
  • All data and code used in the work are publicly available.

A. TIMBRE PRESERVATION EVALUATION

The timbre preservation evaluation tests whether edited audio retains the source instruments’ timbral identity. It uses global MFCC-distribution similarity and average spectral-envelope similarity, alongside instrument-change edits.

  • Timbre context preservation evaluates whether x′ still sounds as though it were played on the same instruments as x.
  • Timbre metrics: SKL Similarity compares the global timbral content of x and x′ using MFCC feature sequences summarized by multivariate Gaussians.
  • Timbre metrics: The bounded SKL-based similarity ranges from 0 to 1, with 1 indicating identical timbral distributions and values near 0 indicating disjoint timbre-space regions.
  • Timbre metrics: Mean MFCC Cosine compares the time-averaged MFCC frame vectors of x and x′ to measure similarity between their average spectral envelopes.
  • Timbre metrics: Mean MFCC Cosine ranges from −1 to 1, where 1 means the recordings share the same average spectral envelope.
  • Evaluation edits: Additional evaluations change a grand piano into an electric piano or acoustic guitar to assess timbre context preservation.

B. OBJECTIVE VALIDATION SETUPS

This section provides details of the objective validation setups referenced earlier in the paper.

  • The section details the objective validation setups referenced in Section 3.1.

B.1 Definition of the Editing Operations

Table 6 summarizes the transformations used in the evaluation and identifies the music facet targeted by each operation.

  • Table 6 lists the transformations applied during objective validation.
  • Each operation is abbreviated in Table 2 and Table 5 for reporting.
  • The table links every operation to the facet it targets.

B.2 Evaluation Set and Audio Rendering

The evaluation uses controlled MIDI material and renders original and edited versions with matched synthesis conditions. Tables 5 and 6 document timbre validation and the ten single-facet operations.

  • 50 MIDI files from the Lakh MIDI Dataset were selected for identifiable melody, stable meter, and recognizable sectional form.These properties make facet-targeted edits yield well-defined changes.
  • Symbolic-domain editing changes one intended facet while leaving the others untouched by construction.
  • Table 5 evaluates two instrument-replacement edits against each sample’s Grand Piano render.
  • Table 6 defines ten objective-validation operations, each targeting one music facet while leaving the remaining facets unchanged.

B.3 Derivation of the Expected Values

Expected metric values are derived from the known effects of controlled edits on musical facets. The derivations predict which preservation metrics should decline and which should remain near their ceilings, while a human questionnaire tests perceptual alignment.

  • Expected entries are derived from each metric’s definition under a known edit rather than selected post hoc.
  • Harmonic Transpositions: Harmonic transpositions should reduce ChromaSim and ChromaDTWS while leaving rhythmic and structural metrics near their preservation ceiling.Pitch-class distributions rotate without changing shape, and transpositions do not alter onset times or sectional layout.
  • Structural Reorderings: Structural reorderings should preserve harmonic metrics and ∆BPM but reduce BeatF and IG after reordered boundaries displace beat positions.
  • Rhythmic Perturbations: A +50% tempo scaling should increase ∆BPM and reduce BeatF and IG, whereas a constant offset should keep ∆BPM near 0 and reduce BeatF more than IG.The offset exceeds the ± 70ms matching tolerance, causing BeatF to collapse while IG remains moderate.
  • Melodic Transformations: Global interval inversion should lower ContourDTWS and MotifRec more than localized climax inversion, while other facet metrics remain high.The contour inversion changes melody pitch-class trajectories and interval 3-grams, whereas localized inversion affects only a small fraction of the interval sequence.
  • The human questionnaire compares two strengths of the same facet-targeted operation against an original clip to assess metric alignment with auditory perception.
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