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Multimodal Concert Video Editing: System Design and Exploratory Evaluation

Lennart Schiweck, Adrian Kempf, Michelle Retig, Moritz Feucht, Tim Mangliers, Benjamin Himpel

arXiv:2610.35816v1cs.MMcs.CVcs.SD

TL;DR

Multi-camera concert editing must coordinate musical relevance, visual quality and cut timing, not simply rank isolated images. This paper presents a modular framework combining audio and visual cues, beat-based timing and probabilistic camera selection. In one four-camera concert study, score-driven edits received mixed assessments, motivating further work on contextual and sequence-level criteria.

  • Problem

    Concert editing must coordinate musical activity, image quality, framing and sequence progression, so ranking isolated images is insufficient.

    Source: §1 · p. 1

  • Method

    A modular system combines instrument-role and image-quality cues in interval-level camera scoring with beat-based timing and probabilistic camera selection.

    Source: §1 · p. 1, §3 · p. 3

  • Results

    With effectively matched automatic timing, all three raters graded the prediction-driven edit less favorably than the random edit; the annotation-driven edit improved on predictions for two raters but was not graded better than random.

    Source: §8 · p. 9

  • Takeaways & Limitations

    The findings motivate testing musical relevance, suitable moments, framing and motivated reactions as distinct factors, with eligibility, aggregation and sequence context independently testable.

    Source: §8 · p. 9

    The evaluation covers one concert, one camera arrangement, one realisation per automatic method and three experts, so it characterizes this application rather than establishing a general ranking.

    Source: §7.4 · p. 9

Abstract

from arXiv · show

Editing multi-camera concerts requires coordinated decisions about musical relevance, visual quality and cut timing. We present a modular framework that combines instrument activity from multitrack audio, visible instruments and image sharpness with beat-based timing and probabilistic camera selection. An interpretable scoring model links these components to the choice of views over complete shot intervals. Component tests and an exploratory expert study on a four-camera concert yield mixed assessments across the editing variants. The findings motivate extending local audiovisual relevance with contextual and sequence-level criteria. The architecture provides a basis for testing these extensions and studying how component behaviour affects the quality of complete edits.

1 Introduction

Multi-camera concert editing must coordinate musical relevance, image quality, framing, sequence progression and cut timing rather than rank isolated images. The paper proposes a modular framework and evaluates human, random and score-driven edits to motivate contextual and sequence-level extensions.

1 Introduction

  • Concert editing must choose both when to cut and which view to show, balancing musical activity, image quality, framing and sequence progression.A useful view may show an active instrument, a reaction or an interaction elsewhere in the ensemble.Source: §1 · p. 1
  • Prior work spans lecture, concert, social-camera, theatre and learned editing systems, including methods that model temporal or candidate-view context.The introduction cites concert-directing systems combining auditory and visual instrument information, learned next-view selection and cut prediction [6], and contextual editing approaches [12].Source: §1 · p. 1
  • The framework separates audio and visual analysis, camera scoring and temporal selection, using beat and tempo estimates to set candidate cut boundaries independently of view selection.Named stems supply instrument activity and roles; visual detections describe instrument visibility, while image-gradient statistics characterize sharpness.Source: §1 · p. 1
  • The paper contributes a transparent multimodal architecture, an explicit mapping from role and quality cues to interval-level camera choices, and component and expert evaluation.The study compares a human reference, random selection and two score-driven variants.Source: §1 · p. 1
  • The mixed results motivate research on shot suitability, contextual reactions and sequence structure, without treating component accuracy as a substitute for complete-edit quality.The proposed directions extend the framework beyond local audiovisual relevance.Source: §1 · pp. 1–2

2 Related work

Related work ranges from explicit cue-based camera scoring to learned view selection and context- or sequence-aware editing. The paper positions its modular connection between instrument roles, quality cues, timing and camera choice alongside these approaches.

2 Related work

  • Wang et al. provide a close explicit-score comparator, combining audible–visible instrument matching, blur and shot-scale information.Table 1 compares system designs across audio inputs, scoring, timing and selection, not performance on a common dataset.Source: §2 · p. 2, Table 1 · p. 2
  • Concert mashups combine perceptual image quality, diversity and cut-point suitability, while other systems model finite-horizon camera scheduling or theatre camera selection and cropping.Shrestha et al. evaluate automatic, manual and random edits; Daniyal and Cavallaro include content quality and switching costs; Stoll et al. hold cut times, shot lengths and shot sizes fixed in a perspective comparison.Source: §2 · p. 2
  • Learned concert-editing work includes visual next-shot selection and systems that separate cut-time prediction from view selection.Jiménez et al. assess static image choices, whereas Gonzálbez-Biosca et al. [6] report cut-prediction and retrieval measures rather than perceived quality of a complete realised edit.Source: §2 · p. 2
  • Multi-camera context models represent past frames and candidate views, but their reference-decision agreement measures do not establish perceptual quality of complete concert edits.Rao et al. [12] introduce the TVMCE benchmark; Cabacas-Maso et al. use a dual-transformer architecture with cross-attention.Source: §2 · p. 2
  • Adjacent editorial systems make sequence-level objectives explicit through action, continuity, rhythm, dialogue, saliency, speaker or affective cues.Their different materials and designs prevent transferring their performance claims to this pilot; these approaches motivate testing editorial context and sequence-level criteria for instrumental performance and ensemble interaction.Source: §2 · pp. 2–3
  • This framework emphasizes an explicit, modular connection between instrument roles, quality cues, shot timing and camera choice, assessed through both component measurements and complete edits.These assessments provide complementary views of the design.Source: §2 · p. 3

3 System overview and material

The system processes synchronized camera streams and concert audio through audio and visual analysis, then combines their cues for interval-level camera selection. The material and annotation-driven variant define the scope of the evaluation.

3 System overview and material

  • The framework processes four synchronized camera streams and concert audio, with stems supporting instrument activity and audible roles and their mono mix supporting beat and tempo estimates.The visual branch uses a pretrained D-FINE detector with retained instrument categories and image-gradient statistics for sharpness; timing defines the intervals over which views are evaluated.Source: §3 · p. 3
  • Figure 1 shows named stems feeding activity analysis and a mono timing waveform, while audible and visual roles and sharpness feed camera utility over shot-boundary intervals.Camera selection produces the edit, and reference annotations provide an alternative label input for the annotation-driven variant.Source: Figure 1 · p. 3
  • The expert study uses four complete edits of about 7 min 48 s from one quartet performance recorded with an audience-oriented view, two close-ups and a wide view.Component measurements cover the first 480 s; the annotation vocabulary includes six audio labels, and the annotation-driven variant adds pan and clapping cues.Source: §3 · p. 3
  • Table 2 maps cameras and reports the reference supports for not-blurred, partially blurred and fully blurred labels.Its n denotes the number of one-second blur-evaluation samples.Source: Table 2 · p. 4
  • The prediction-driven variant uses estimated audio roles, visible roles and blur, whereas the annotation-driven variant substitutes reference labels and adds pan and clapping cues.The latter tests annotation-driven camera scoring rather than a detector-only substitution.Source: §3 · p. 4

4 Camera scoring and selection

The system scores each camera from audible–visible role matches and quality cues, aggregates scores across candidate shot intervals, and samples a non-repeating view. Beat-based timing sets candidate boundaries, while interval scoring uses future context in this offline implementation.

4.1 Local utility

  • Per-second camera utility matches audible primary and secondary roles with visible roles, applies match bonuses and role-mismatch penalties, and uses a quality multiplier for blur and pan cues.The annotation-driven variant uses pan cues; the multiplier is zero for a fully blurred or recognised pan label and otherwise one, while partial blur is not penalized.Source: §4.1 · p. 4, §4 · p. 4

4.2 Timing, interval utility and selection

  • Candidate cut boundaries occur on every fourth tracked beat, with target durations sampled by tempo class; conservative aggregation uses the interval’s integer-bin scores before camera selection.For at most ten bins, the aggregate is the minimum clipped at zero. Proportional sampling excludes the previous camera; if the denominator is not positive, selection is uniform over the other three cameras, so a zero score is not a hard veto.Source: §4.2 · p. 4
  • The timing rule assumes four beats per bar without establishing true downbeats or phrasing, and full-interval scoring and centred audio analysis use future context in an offline implementation.The no-repeat constraint is not a sequence model of editorial grammar.Source: §4.2 · pp. 4–5

5 Component performance

Audio-role estimation is moderately accurate, while blur classification depends strongly on camera; these differences affect how the cues can guide camera choice.

5 Component performance

  • Audio-role estimation reaches support-weighted F1 scores of 0.654 for primary roles and 0.601 for secondary roles.Performance varies across instruments.Source: §5 · p. 5
  • Blur classification is strongly camera-dependent, limiting how uniformly blur cues can guide camera choice.The passage reports camera dependence but gives no per-camera scores.Source: §5 · p. 5
  • Differences in audio-role and blur performance affect how the corresponding cues can guide camera choice.Appendix B reports per-class audio and blur results, sampling definitions, role timelines, and camera-score plots.Source: §5 · p. 5

6 Expert evaluation

Three experts assessed four edits of one concert, with mixed overall grades and contrasting feedback about editorially useful views. The automatic variants had nearly matched cut timing, but their assessments differed.

6.1 Study design

  • The three experts assessed a human reference, a random-selection edit, a predicted-score edit, and an annotation-driven-score edit without knowing the editing method.The human reference was student-produced under expert supervision.Source: §6.1 · p. 5
  • Only overall grades are interpreted directionally because conflicting scale anchors make the detail ratings ambiguous.The questionnaire also requested ratings of cut timing, transitions, picture quality, coherence, and aesthetic impression.Source: §6.1 · p. 5

6.2 Cut timing and shot durations

  • Automatic cut boundaries differ by at most one frame, so V2–V4 have effectively the same pacing for comparing view choices.V3 and V4 boundaries coincide, while V2 is one frame earlier; V1 has 35 internal camera changes and each automatic edit has 65.Source: §6.2 · p. 5
  • V1’s music-window mean shot duration is 10.86 s, compared with 6.79 s for V2–V4.V1 starts with an uninterrupted 116.72-s shot and makes no announcement cuts, while the automatic edits make nine.Source: §6.2 · p. 5

6.3 Overall grades

  • All three experts grade V3 less favourably than V2, while V4 improves on V3 for two experts but never grades better than V2.V1 receives consistent overall grades, whereas V2 and V4 show greater disagreement; overall score-driven selection has no advantage over random selection in this recording.Source: §6.3 · pp. 5–6

6.4 Written feedback

  • The written-feedback analysis preserves positive, critical, and differing expert assessments rather than reducing them to consensus.It describes experts’ expressed judgements without assigning an internal technical cause to each shot.Source: §6.4 · p. 6
  • Written feedback values musical action, acceptable framing, motivated reactions, and ensemble interaction, while distinguishing usable images from editorially useful moments.Criticism of incidental audience views coexists with praise for motivated reactions, so the comments do not support a blanket audience-view prohibition.Source: §6.4 · p. 6

7 Discussion

The discussion relates component behaviour to complete-edit judgements but does not isolate a single cause of the outcomes. It identifies limits in the current objective and proposes controlled comparisons, within a study of one concert and three experts.

7.1 Relating system components to editorial assessment

  • The evaluation links component-label agreement, timing, and complete-sequence assessment, but does not identify a single causal component behind the differing edits.V4’s improvement over V3 for two raters cannot isolate recognition accuracy because annotation source, available cues, and potentially random draws change together.Source: §7.1 · p. 6
  • Experts’ criteria span musical-action coverage, acceptable framing, suitable moments, motivated reactions, and ensemble interaction.The proposed links between feedback and operations motivate tests; they do not establish that an operation caused a reported shot.Source: §7.1 · p. 7

7.2 Tracing component outputs into camera choices

  • CAM1’s partial/not-blurred disagreement yields poor classification metrics but leaves its score unchanged because both states have Qc = 1.By contrast, audible-role changes affect bonuses and penalties, while full-blur or recognised-pan labels can zero a per-second score; interval aggregation, prior camera, and fallback still shape the displayed view.Source: §7.2 · p. 7
  • The current objective misses distinctions between a relevant instrument and a suitable action, and between a leading instrument and a valuable reaction.Audience presence and a non-leading musician are not inherently unsuitable, and selection cannot create an unavailable close-up.Source: §7.2 · p. 8

7.3 Proposed controlled comparisons

  • Proposed comparisons separate cue availability and reliability, candidate eligibility, contextual relevance, and sequence policy so each can be tested independently.The framework also proposes phase-specific timing states and per-rater reporting of reaction value and pace preference.Source: §7.3 · p. 8
  • A suitability comparison would add independently annotated eligibility or contextual reaction value while holding other inputs fixed.Suggested outcomes include unsuitable-view exposure, musical-action coverage, and paired sequence preference; the value of these objectives for this concert remains unmeasured.Source: §7.3 · p. 8
  • A sequence-context comparison would hold local scores and timing fixed while testing a prespecified history or transition term.The proposal calls for several prespecified implementations and blinded sequence judgements, since reference-decision agreement alone is insufficient.Source: §7.3 · p. 9
  • Quality and fallback tests would distinguish hard eligibility from soft preference, while reporting that holds can change realised cuts and dwell times.The pilot motivates these tests but establishes no improvement from them.Source: §7.3 · p. 9

8 Conclusion

The framework combines multimodal scoring, beat-based timing and probabilistic camera selection, but expert assessments of the automatic edits are mixed. Component results and expert feedback motivate extending the selection objective and policy beyond detector accuracy.

8 Conclusion

  • With effectively matched automatic timing, all three raters graded the prediction-driven edit less favourably than the available random edit; annotation-driven selection improved on predictions for two raters but did not beat random.The findings motivate further development of the selection objective and policy.Source: §8 · p. 9
  • The system combines audio and visual analysis, explicit camera scoring, beat-based timing and probabilistic selection in a modular editing framework.Source: §8 · p. 9
  • The research agenda extends beyond detector accuracy to separately test relevance, suitable moments, framing, motivated reactions, quality-label effects, eligibility, aggregation and sequence context.Controlled comparisons can trace how changes propagate from labels to scores, selected views and complete-edit judgement.Source: §8 · p. 9

Declarations

The appendix documents the study’s acknowledgements and implementation, including beat-grid timing, stem-based activity and role estimation, visual instrument and sharpness cues, and camera scoring.

Declarations

  • Funding came in part through the DATIpilot programme; the authors acknowledge editing, reference-edit, production and student contributors, and report AI assistance with manuscript and figure preparation, calculations and feedback grouping.The authors retain responsibility for the scientific content and submitted version.Source: Declarations · pp. 9–10

A Implementation details

  • The implementation appendix specifies the audio, visual and selection modules, with the camera mapping and class supports listed in Table 2.Source: A Implementation details · p. 12

A.1 Beat grid and target shot duration

  • The timing module builds an approximate bar grid from detected beats, uses tempo classes to sample target shot durations, and aligns each cut to the first grid point at or after the target.The grid assumes four beats per bar and uses the first detected beat as phase; it does not estimate metre or true downbeats, and realised shots can exceed the sampled target after alignment.Source: §A.1 · p. 12

A.2 Activity and role estimation from audio stems

  • Named audio stems provide direct energy measurements, which are smoothed and normalised across stems before thresholding, role assignment and semantic label mapping.The nine-frame moving average has approximately 15 s of support, can obscure short entrances and uses future context; shared normalisation bounds also leave relative stem levels dependent on gain and microphone bleed.Source: §A.2 · pp. 12–13

A.3 Visible instruments and image sharpness

  • The visual module detects selected instruments and uses bounding-box area as a primary-status proxy, while image-gradient statistics provide sharpness labels.The detector is not fine-tuned on the concert, and the size proxy does not assess occlusion, face visibility, aesthetics or whether an instrument is played; blur thresholds depend on resolution and image conditions.Source: §A.3 · p. 13

A.4 Camera utility from role matches and penalties

  • Camera scoring matches audible primary and secondary labels to visible instruments, applies penalties for missing or extra labels, and reduces scores for fully blurred footage or recognised pan labels.The prediction-driven variant uses blur and instrument-role estimates; the annotation-driven variant also uses pan and clapping labels.Source: §A.4 · pp. 13–14

A.5 Lower-tail interval aggregation and camera sampling

The system aggregates camera scores over prospective shot intervals using a clipped lower-tail mean, then samples a camera subject to a no-repeat constraint. The aggregation is conservative, while the transition rule is not a learned sequence model.

A.5 Lower-tail interval aggregation and camera sampling

  • For candidate interval [t, t′), utility is the clipped mean of its lowest m integer-second-bin scores; with n ≤10 bins, it reduces to the minimum score clipped at zero.Missing score bins are zero, and brief failures can reject an otherwise useful shot.Source: §A.5 · p. 15
  • The next camera is sampled for the whole interval from a categorical distribution based on eligible camera weights, with a uniform fallback over the other three cameras when weights vanish.Source: §A.5 · p. 15
  • Selection enforces a first-order no-repeat constraint rather than a learned editing grammar, and a zero utility is not a hard veto because the all-zero fallback can still select an unsuitable view.Source: §A.5 · p. 15
  • Speech and singing share one event label, and the same timing policy applies throughout the clip; a separate announcement policy remains future work.Source: §A.5 · p. 15

B Component evaluation

Component evaluation finds uneven audio-role and blur classification, then documents temporal and expert-feedback analyses and how score changes may fail to reach selected views.

B.1 Sampling and metric definitions

  • Role metrics are separate primary and secondary multilabel tasks over six instruments; blur evaluation treats unlabelled instants as not blurred, and adjacent samples are dependent.Audio-role evaluation uses integer bins over intervals; weighted summaries are not micro-averages.Source: §B.1 · pp. 15–16

B.2 Audio roles

  • Speech primary labels reach F1 0.944, while primary Drums reach recall 0.021 and F1 0.041 despite 95 positive reference samples.Zero reference support for primary Bass and all active Piano roles precludes detection claims for those classes.Source: §B.2 · p. 16, Table B.1 · p. 16

B.3 Blur classification

  • CAM1 is predicted not blurred despite 474/481 partially blurred references and has near-zero weighted F1; partial blur is not penalized in scoring.CAM2/CAM3 partial-blur detections have high precision but low recall, while CAM4 has no blurred reference samples.Source: §B.3 · p. 16, Table B.2 · p. 16

C Additional temporal documentation

  • The four edits contain 230 internal camera changes; boundary-sensitivity uses 33 complete music shots for V1 and 54 for each automatic variant.Longer, more variable manual shots versus shorter automatic shots are descriptive, not a statistical test of musical appropriateness.Source: C Additional temporal documentation · p. 18

D Questionnaire dimensions

E Individual expert responses

F Descriptive analysis of written feedback

  • Thematic reading groups whole comment fields, preserves disagreements, and treats themes as expressed judgments—not prevalence estimates or technical causes; AI grouping assistance is disclosed.Source: F Descriptive analysis of written… · p. 19

G Score sensitivity and camera-selection effects

  • A visibility-label change can raise per-second score by 4.9 when Qc = 1, but aggregation, eligibility, normalization, and sampling can leave selected views unchanged.Lower-tail aggregation and clipping at zero can leave interval utility unchanged; none of these score or choice effects alone establishes improved editorial quality.Source: G Score sensitivity and… · p. 19
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