标题： WoW-Bench：通过海洋哺乳动物叫声评估音频-语言模型中的细粒度声学感知 显示英文标题

标题： WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations

摘要： 大型音频语言模型（LALMs）将语言理解扩展到听觉领域，但它们在执行低层次聆听任务（如音高和持续时间检测）的能力仍鲜有研究。 然而，低层次聆听对于现实世界中的分布外任务至关重要，其中模型必须基于细致的声学线索对不熟悉的声音进行推理。 为解决这一差距，我们引入了鲸类世界基准（WoW-Bench），以使用海洋哺乳动物叫声评估低层次的听觉感知和认知。 WoW-bench由一个用于分类新声音的感知基准和一个受布鲁姆分类法启发的认知基准组成，用于评估模型记住、理解、应用和分析声音事件的能力。 对于认知基准，我们还引入了干扰问题，以评估模型是否真正通过聆听解决问题，而不是依赖其他启发式方法。 与最先进的LALMs进行的实验显示，其性能远低于人类水平，表明LALMs需要更强的听觉基础。 显示英文摘要

摘要： Large audio language models (LALMs) extend language understanding into the auditory domain, yet their ability to perform low-level listening, such as pitch and duration detection, remains underexplored. However, low-level listening is critical for real-world, out-of-distribution tasks where models must reason about unfamiliar sounds based on fine-grained acoustic cues. To address this gap, we introduce the World-of-Whale benchmark (WoW-Bench) to evaluate low-level auditory perception and cognition using marine mammal vocalizations. WoW-bench is composed of a Perception benchmark for categorizing novel sounds and a Cognition benchmark, inspired by Bloom's taxonomy, to assess the abilities to remember, understand, apply, and analyze sound events. For the Cognition benchmark, we additionally introduce distractor questions to evaluate whether models are truly solving problems through listening rather than relying on other heuristics. Experiments with state-of-the-art LALMs show performance far below human levels, indicating a need for stronger auditory grounding in LALMs.