arXiv:2606.31338v1 Announce Type: cross Abstract: Recent music audio-language models achieve high accuracy on instrument question-answering benchmarks, but it remains unclear whether this reflects robust audio grounding or benchmark-specific shortcuts. In this paper, we introduce an OpenMIC-derived diagnostic benchmark sequence for instrument grounding in music audio-language models, extending binary instrument-presence QA to genre-prior-reduced examples, confusable instrument discrimination, longer audio context, and temporal localization. Across these settings, high binary QA accuracy often
The proliferation of music audio-language models necessitates more rigorous evaluation to move beyond superficial performance metrics to truly understand model capabilities and limitations.
This research provides a critical diagnostic tool for assessing the robustness of AI in understanding complex audio information, which is foundational for numerous AI applications beyond music.
Evaluations of music audio-language models will shift from simplistic binary QA to more nuanced, diagnostic benchmarks, potentially revealing limitations previously masked by high accuracy scores.
- · AI researchers and developers
- · Music technology companies focusing on AI
- · Ethical AI development
- · Over-hyped music AI models
- · Benchmarks lacking depth
Improved diagnostic benchmarks will lead to a clearer understanding of the actual capabilities and shortcomings of music audio-language models.
This understanding will guide the development of more robust and truly 'grounded' AI models capable of nuanced audio interpretation.
Better audio grounding in AI could eventually accelerate advancements in areas like sound-based environmental monitoring, medical diagnostics, and more sophisticated human-computer interaction based on auditory cues.
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Read at arXiv cs.AI