SpotSound：通过细粒度时间定位增强大型音频语言模型

SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding

April 14, 2026

作者: Luoyi Sun, Xiao Zhou, Zeqian Li, Ya Zhang, Yanfeng Wang, Weidi Xie

cs.AI

摘要

近期，大型音频语言模型（ALM）在整体音频理解方面展现出卓越能力，但在时序定位任务中仍存在不足——即难以精准确定长音频中事件发生的具体时间点。这一局限源于两个因素：训练数据主要采用缺乏精确时间戳的片段级监督，且现有基准测试未能模拟短事件被密集背景音掩盖的真实场景。本文提出SpotSound，一种专用于音频事件定位的音频语言模型。该模型引入了创新的训练目标，专门用于抑制对输入音频中不存在事件的时间戳幻觉。同时，我们推出SpotSound-Bench这一挑战性时序定位基准，其中目标事件仅占每个音频片段约10%的时长，形成严格的"大海捞针"式评估。实验表明，SpotSound在时序定位基准测试中达到最先进水平，同时在下游通用音频语言任务中保持稳健性能。代码、模型及基准测试数据已发布于https://loiesun.github.io/spotsound/

English

Large Audio-Language Models (ALMs) have recently demonstrated remarkable capabilities in holistic audio understanding, yet they remain unreliable for temporal grounding, i.e., the task of pinpointing exactly when an event occurs within long-form audio. This limitation stems from two factors: training data dominated by clip-level supervision lacking precise timestamps, and benchmarks that fail to simulate real-world scenarios where short events are obscured by dense background sounds. In this paper, we introduce SpotSound, an audio language model designed for grounding audio events. SpotSound incorporates a novel training objective, specifically designed to suppress hallucinated timestamps for events absent from the input. Additionally, we present SpotSound-Bench, a challenging temporal grounding benchmark where target events occupy less than ~10\% of each clip, creating a rigorous `needle-in-a-haystack' evaluation. Experiments demonstrate that SpotSound achieves state-of-the-art results on temporal grounding benchmarks while maintaining robust performance across general downstream audio-language tasks. Code, models and benchmark are released on https://loiesun.github.io/spotsound/