Large Audio Language Models (LALMs) have been widely applied in real-time scenarios, such as in-car assistants and online meeting comprehension. In practice, audio inputs are often corrupted by device and environmental noise, leading to performance degradation. However, existing LALM studies on noise lack quantitative analysis and rely mainly on intuition and empirical observation, thus failing to understand practical robustness. To address this issue, we introduce Signal Embedding Energy (SEE), a method for quantifying the impact of noise intensity on LALM inputs, enabling the differentiation of LALM robustness in real-world deployments. SEE introduces a perspective based on structured activation subspaces derived from the model's internal representations, which more accurately captures its perception of noise than raw audio features. Across experiments, SEE exhibits a strong correlation with LALM performance, achieving a correlation of 0.98. Surprisingly, traditional audio denoising methods are only marginally effective for LALMs, and, in some cases, even increase SEE and impair performance. This suggests a mismatch between speech-centric denoising objectives and the noise sensitivity of modern LALMs. Therefore, we propose a mitigation strategy derived from SEE to denoise LALM inputs, outperforming existing denoising methods. This paper introduces a novel metric for noise quantification in LALMs, providing guidance for robustness improvements in real-world deployments.

翻译：大型音频语言模型（LALMs）已广泛应用于实时场景，如车载助手和在线会议理解。在实践中，音频输入常受到设备和环境噪声的干扰，导致性能下降。然而，现有关于噪声的LALM研究缺乏定量分析，主要依赖直觉和经验观察，因而未能理解其实际鲁棒性。为解决这一问题，我们提出了信号嵌入能量（SEE），这是一种量化噪声强度对LALM输入影响的方法，能够区分LALM在实际部署中的鲁棒性。SEE引入了一种基于模型内部表示的结构化激活子空间的视角，相比原始音频特征，它能更准确地捕捉模型对噪声的感知。在各项实验中，SEE与LALM性能表现出强相关性，相关系数达到0.98。令人惊讶的是，传统的音频去噪方法对LALMs仅略微有效，在某些情况下甚至会增加SEE并损害性能。这表明以语音为中心的去噪目标与现代LALMs的噪声敏感性之间存在不匹配。因此，我们提出了一种基于SEE的缓解策略来对LALM输入进行去噪，其性能优于现有去噪方法。本文提出了一种用于LALMs噪声量化的新指标，为实际部署中的鲁棒性改进提供了指导。