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arxiv 2506.23009 v3 pith:TIQXH57A submitted 2025-06-28 cs.CV

MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models

Jian Chen , Wenye Ma , Penghang Liu , Wei Wang , Tengwei Song , Ming Li , Chenguang Wang , Jiayu Qin
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Ruiyi Zhang Changyou Chen
This is my paper
classification cs.CV
keywords musicmllmsdatasetmusixqaunderstandingvisualadvancinglanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Multimodal Large Language Models (MLLMs) have achieved remarkable visual reasoning abilities in natural images, text-rich documents, and graphic designs. However, their ability to interpret music sheets remains underexplored. To bridge this gap, we introduce MusiXQA, the first comprehensive dataset for evaluating and advancing MLLMs in music sheet understanding. MusiXQA features high-quality synthetic music sheets generated via MusiXTeX, with structured annotations covering note pitch and duration, chords, clefs, key/time signatures, and text, enabling diverse visual QA tasks. Through extensive evaluations, we reveal significant limitations of current state-of-the-art MLLMs in this domain. Beyond benchmarking, we developed Phi-3-MusiX, an MLLM fine-tuned on our dataset, achieving significant performance gains over GPT-based methods. The proposed dataset and model establish a foundation for future advances in MLLMs for music sheet understanding. Code, data, and model will be released upon acceptance.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence

    cs.SD 2026-04 unverdicted novelty 7.0

    ONOTE is a multi-format benchmark that applies a deterministic pipeline to expose a disconnect between perceptual accuracy and music-theoretic comprehension in leading omnimodal AI models.

  2. Music I Care About: Automated Multimodal Benchmarking of LLM Music Perception Skills on (Almost) Any Music

    cs.SD 2026-07 unverdicted novelty 6.0

    A meta-benchmark that auto-generates multimodal music-perception multiple-choice tests from user symbolic music, demonstrated on ChoraleBricks with text-only and white-noise controls.

  3. Direct content-based retrieval from music scores images

    cs.CV 2026-05 unverdicted novelty 5.0

    Compares OMR-based, direct transformer, and LLM approaches for content-based retrieval in music score images across four corpora, finding OMR stronger in-domain and transcription-free models better for variability.

  4. Direct content-based retrieval from music scores images

    cs.CV 2026-05 unverdicted novelty 4.0

    Evaluates OMR-based, transcription-free Transformer, and LLM approaches for content-based retrieval in music score images on four diverse corpora, concluding OMR excels in-domain while transcription-free models handle...