arXiv:2605.29300v1 Announce Type: cross Abstract: Recent Large Audio-Language Models (LALMs) have demonstrated promising abilities in understanding musical content. However, whether their responses are grounded in the correct temporal regions of the audio remains underexplored. This limitation is particularly critical for music understanding, where key information often occurs as temporally localized events, such as instrument entries and rhythmic transitions. To address this gap, we introduce MusTBENCH, a music-expert-validated benchmark designed to evaluate temporal grounding in LALMs throug
The proliferation of Large Audio-Language Models necessitates a robust framework for evaluating their musical understanding, especially concerning temporal grounding, to ensure their practical utility.
Accurate temporal grounding in LALMs is crucial for tasks requiring precise musical event identification, impacting content creation, analysis, and human-computer interaction in music.
The introduction of MusTBENCH provides a standardized and expert-validated benchmark for assessing a critical, previously underexplored, aspect of LALM performance in music.
- · AI researchers in music
- · Music technology companies
- · Generative AI platforms
- · Audio software developers
- · LALMs with poor temporal grounding
- · Outdated music analysis tools
Improved LALM architectures and training methodologies that specifically address temporal grounding challenges.
Development of more sophisticated AI-powered music production and analysis tools capable of understanding nuanced musical events.
Enhanced human-AI collaboration in music, leading to novel forms of musical expression and more efficient content creation workflows.
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Read at arXiv cs.AI