Spatial audio understanding aims to enable machines to interpret complex auditory scenes, particularly when sound sources move over time. In this work, we study Spatial Audio Question Answering (Spatial AQA) with a focus on movement reasoning, where a model must infer object motion, position, and directional changes directly from stereo audio. First, we introduce a movement-centric spatial audio augmentation framework that synthesizes diverse motion patterns from isolated mono audio events, enabling controlled and scalable training data generation. Second, we propose an end-to-end multimodal finetuning approach with a thinking mode, which allows audio-language models to produce explicit intermediate reasoning steps before predicting an answer. Third, we investigate the impact of query-conditioned source separation as a preprocessing stage and compare three inference regimes: no masking, an audio grounding model (AGM), and ground-truth masks. Our results show that reasoning amplifies the benefits of source separation, with thinking mode showing significant improvement of +5.1% when a single event is present in the question. These findings highlight the interplay between movement modeling, reasoning, and separation quality, offering new insights for advancing spatial audio understanding.

翻译：空间音频理解旨在使机器能够解析复杂的听觉场景，尤其是在声源随时间移动的情况下。本研究聚焦于运动推理的空间音频问答任务，要求模型直接从立体声音频中推断物体的运动、位置及方向变化。首先，我们提出了一种以运动为中心的空间音频增强框架，该框架可从孤立的单声道音频事件合成多样化的运动模式，从而实现可控且可扩展的训练数据生成。其次，我们提出了一种带“思考模式”的端到端多模态微调方法，使音频-语言模型能够在预测答案前生成显式的中间推理步骤。第三，我们探究了以查询为条件的声源分离作为预处理阶段的影响，并比较了三种推理机制：无掩码、音频定位模型以及真实掩码。实验结果表明，推理过程放大了声源分离的效益，在问题中仅存在单一事件时，“思考模式”带来了+5.1%的显著性能提升。这些发现揭示了运动建模、推理与分离质量之间的相互作用，为推进空间音频理解提供了新的见解。