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arXiv · 2610.15773

HMU-Bench: Hierarchical Music Understanding Benchmark for Omni Models

Zheqi Dai·Boyang Wang·Zhiyue Wu·Yiyang Xu·Xinming Shi·Ruibin Yuan·Xuerui Yang·Chao Yan·Qiuqiang Kong
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Abstract

Music-understanding benchmarks should identify both the evidence available to a model and the operations a correct answer requires. We introduce HMU-Bench, a bilingual benchmark with notation and audio components. ABC Q&A contains 1,029 Chinese-English question pairs across six musical domains, labeled as extraction, single-step inference, or multi-step inference. Audio Q&A contains 460 question pairs on 92 recordings, spanning five scope-difficulty configurations. All music is Suno-generated, and questions and references are human-reviewed. Reference-based evaluation of seven omni models yields high notation scores for the strongest systems, but no model averages above 1.4 on the 0-4 audio scale. Notation and audio rankings show little correlation. A transposition case study also reveals octave errors missed by LLM grading. These findings motivate separate modality reporting and event-level checks for symbolic outputs. Data, annotations, evaluator prompts, and graded responses will be released at https://github.com/stepaudiollm/hmu-bench.

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Zheqi Dai, Boyang Wang, Zhiyue Wu, Yiyang Xu, Xinming Shi, Ruibin Yuan, Xuerui Yang, Chao Yan, Qiuqiang Kong. 2026-10-11. HMU-Bench: Hierarchical Music Understanding Benchmark for Omni Models. https://arxiv.org/abs/2610.15773

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