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arxiv: 2505.20638 · v2 · submitted 2025-05-27 · 💻 cs.SD · cs.CV· cs.MM· eess.AS

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

Wenhao You , Xingjian Diao , Wenjun Huang , Chunhui Zhang , Keyi Kong , Weiyi Wu , Chiyu Ma , Zhongyu Ouyang
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Tingxuan Wu Ming Cheng Soroush Vosoughi Jiang Gui
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Pith reviewed 2026-05-19 14:07 UTC · model grok-4.3

classification 💻 cs.SD cs.CVcs.MMeess.AS
keywords Music AVQAmultimodal large language modelsaudio-visual question answeringspatial-temporal designsmusic-specific modelingdomain-specific approachessystematic review
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The pith

Music audio-visual question answering requires specialized multimodal designs rather than general approaches.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper reviews datasets and methods for Music Audio-Visual Question Answering to pinpoint what enables strong performance. It concludes that general multimodal large language models fall short due to the continuous, densely layered nature of music audio-visual content and its intricate temporal dynamics. A sympathetic reader would care because the analysis isolates concrete design choices, such as specialized input processing and music-specific strategies, that empirically correlate with better results. The work highlights patterns from existing approaches and suggests directions for incorporating musical knowledge to improve multimodal understanding of music.

Core claim

Through a systematic analysis of Music AVQA datasets and methods, this paper identifies that specialized input processing, architectures incorporating dedicated spatial-temporal designs, and music-specific modeling strategies are critical for success in this domain. The study provides valuable insights by highlighting effective design patterns empirically linked to strong performance and proposes concrete future directions for incorporating musical priors.

What carries the argument

Systematic analysis of Music AVQA datasets and methods that isolates specialized input processing, dedicated spatial-temporal designs, and music-specific modeling as critical for performance.

Load-bearing premise

The reviewed datasets and methods are representative of the domain and the identified patterns are causally linked to performance rather than merely correlated with unmeasured factors.

What would settle it

A general multimodal model achieving state-of-the-art results on Music AVQA benchmarks without specialized input processing, spatial-temporal designs, or music-specific strategies would challenge the central claim.

Figures

Figures reproduced from arXiv: 2505.20638 by Chiyu Ma, Chunhui Zhang, Jiang Gui, Keyi Kong, Ming Cheng, Soroush Vosoughi, Tingxuan Wu, Weiyi Wu, Wenhao You, Wenjun Huang, Xingjian Diao, Zhongyu Ouyang.

Figure 1
Figure 1. Figure 1: Contrast between (i) conventional QA and (ii) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Radar plots showing the per-type average [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy comparison of Music AVQA models [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Radar plots showing the per-type average [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of Music AVQA question types spanning audio, visual, and audio-visual modalities, including [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Accuracy comparison of Music AVQA models across representative question types, grouped by modality: [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
read the original abstract

While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Audio-Visual Question Answering (Music AVQA) particularly underscores this, presenting unique challenges with its continuous, densely layered audio-visual content, intricate temporal dynamics, and the critical need for domain-specific knowledge. Through a systematic analysis of Music AVQA datasets and methods, this paper identifies that specialized input processing, architectures incorporating dedicated spatial-temporal designs, and music-specific modeling strategies are critical for success in this domain. Our study provides valuable insights for researchers by highlighting effective design patterns empirically linked to strong performance, proposing concrete future directions for incorporating musical priors, and aiming to establish a robust foundation for advancing multimodal musical understanding. We aim to encourage further research in this area and provide a GitHub repository of relevant works: https://github.com/WenhaoYou1/Survey4MusicAVQA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript surveys Music Audio-Visual Question Answering (Music AVQA) datasets and methods. It claims that general Multimodal Large Language Models are insufficient for this domain due to its continuous, densely layered audio-visual content and temporal dynamics, and that specialized input processing, architectures with dedicated spatial-temporal designs, and music-specific modeling strategies are critical for strong performance. The authors highlight empirical patterns from prior work, propose future directions incorporating musical priors, and provide a GitHub repository of relevant works.

Significance. If the identified design patterns prove robust and generalizable, the survey could usefully guide future work on multimodal musical understanding by distilling effective practices from the literature. Its value would be strengthened by reproducible code or parameter-free derivations, but as a survey relying on existing results without new controlled experiments, its impact hinges on the exhaustiveness and fairness of the dataset/method coverage.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Methods Analysis): The central claim that specialized input processing, dedicated spatial-temporal designs, and music-specific modeling are 'critical' rests on observational correlations between design choices and reported performance. No new ablations, matched comparisons, or controls for model scale and training data volume are presented to isolate these factors from confounders; without such evidence the patterns remain compatible with the alternative that general multimodal scaling plus domain data suffices.
  2. [§3] §3 (Dataset Analysis): The systematic review of datasets must explicitly state inclusion criteria, search strategy, and coverage of all relevant published Music AVQA works (including negative results) to support the representativeness of the identified patterns; otherwise the claimed empirical links to performance cannot be confidently generalized.
minor comments (2)
  1. [Abstract] The GitHub repository link is useful but should include a brief description of its contents and update status in the main text.
  2. [Throughout] Notation for audio-visual features and temporal modeling components should be standardized across sections for clarity.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our survey of Music AVQA. We address each major comment below, clarifying the observational nature of our analysis as a literature review while committing to revisions that improve transparency and precision of claims.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Methods Analysis): The central claim that specialized input processing, dedicated spatial-temporal designs, and music-specific modeling are 'critical' rests on observational correlations between design choices and reported performance. No new ablations, matched comparisons, or controls for model scale and training data volume are presented to isolate these factors from confounders; without such evidence the patterns remain compatible with the alternative that general multimodal scaling plus domain data suffices.

    Authors: We agree that our central observations derive from patterns across published results rather than new controlled experiments. As a survey, we synthesize existing comparisons in the literature where specialized designs are tested against general multimodal approaches, often under comparable training regimes. These patterns motivate our recommendations, but we acknowledge they do not definitively rule out scaling-based explanations. In revision we will moderate the language in the abstract and §4, replacing 'critical' with 'empirically associated with strong performance' and explicitly noting the observational basis of the findings. revision: partial

  2. Referee: [§3] §3 (Dataset Analysis): The systematic review of datasets must explicitly state inclusion criteria, search strategy, and coverage of all relevant published Music AVQA works (including negative results) to support the representativeness of the identified patterns; otherwise the claimed empirical links to performance cannot be confidently generalized.

    Authors: We accept this point and will strengthen the methodological transparency of the survey. The revised §3 will add a dedicated paragraph describing the search strategy (keywords, databases, time window), explicit inclusion/exclusion criteria, and the total number of works screened versus included. We will also note any documented negative or unsuccessful approaches from the literature to provide a balanced account of coverage. revision: yes

standing simulated objections not resolved
  • Conducting new ablations or matched comparisons that control for model scale and data volume, as this lies outside the scope of a survey paper synthesizing existing published results.

Circularity Check

0 steps flagged

Survey analysis with no internal derivations or self-referential reductions

full rationale

This paper is a survey that performs a systematic analysis of existing Music AVQA datasets and methods drawn from prior published literature. Its central claim—that specialized input processing, spatial-temporal architectures, and music-specific modeling are critical—rests on empirical patterns cataloged from external works rather than any new equations, fitted parameters, or predictions generated within the paper itself. No load-bearing steps reduce by construction to the authors' own inputs, self-citations, or ansatzes; the conclusions are presented as observations from independent sources. The derivation chain is therefore self-contained with no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey the central claim rests on the completeness and representativeness of the reviewed literature rather than new axioms, free parameters, or invented entities.

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Reference graph

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