Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

• URL: http://arxiv.org/abs/2607.04383v1
• Date: Sun, 05 Jul 2026 16:22:14 GMT
• Title: Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding
• Abstract summary: We introduce AutoAEG, a scalable pipeline that constructs openvocabulary onset/offset supervision by automatic data construction and model finetuning.<n>Our results show that automatically constructed data, coupled with interval-aware reward function design, is an effective data-side route to expanding the temporal localization capability of LALMs.
• Score: 36.43390300751778
• License: http://creativecommons.org/licenses/by/4.0/
• 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 resources provide open-vocabulary onset/offset supervision, and manual temporal annotation is prohibitively expensive. To address this, we introduce Auto-AEG, a scalable pipeline that constructs such supervision by automatic data construction and model fine-tuning. It pairs programmatically synthesized clips, which carry exact ground-truth intervals for supervised cold-start, with multi-model pseudo-labels on real-world audio that supply the reward signal for reinforcement learning. Training with this pipeline yields promising performance gains on both the DESED SED benchmark and AEGBench, an independent difficulty-stratified benchmark we release. Our results show that automatically constructed data, coupled with interval-aware reward function design, is an effective data-side route to expanding the temporal localization capability of LALMs.

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