arXiv:2607.04383v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level precision only over a closed label set. At the intersection of these paradigms lies the task of Open-Vocabulary Audio Event Grounding: predicting all time intervals of a target sound event described by an arbitrary natural language query. While this task is crucial for real-world audio understanding and LALM adaptation, it is bottlenecked by data scarcity. Few large-scale
The rapid advancement of Large Audio-Language Models (LALMs) highlights the limitations of current sound event detection, creating an immediate need for scalable data solutions to enable open-vocabulary grounding.
This development addresses a critical bottleneck in LALM capabilities, bridging the gap between fluent sound reasoning and precise real-world audio comprehension, which is essential for advanced AI applications.
The ability to construct scalable data for Open-Vocabulary Audio Event Grounding allows LALMs to move beyond closed label sets and understand arbitrary natural language queries about sound events with high precision.
- · AI researchers
- · LALM developers
- · Audio analysis software providers
- · Robotics
- · Traditional closed-set sound event detection methods
- · Developers reliant on manual audio data annotation
Improved performance and broader applicability of Large Audio-Language Models in real-world scenarios.
New AI applications emerge that rely on precise open-vocabulary audio understanding, such as advanced surveillance or human-robot interaction.
The development of more sophisticated and nuanced AI agents capable of interpreting complex auditory environments to inform decision-making.
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Read at arXiv cs.AI