Abstract
Recent progress in auditory intelligence has yielded high-performing systems for sound event detection (SED), acoustic scene classification (ASC), automated audio captioning (AAC), and audio question answering (AQA). Yet these tasks remain largely constrained to surface-level recognition-capturing what happened but not why, what it implies, or how it unfolds in context. I propose a conceptual reframing of auditory intelligence as a layered, situated process that encompasses perception, reasoning, and interaction. To instantiate this view, I introduce four cognitively inspired task paradigms-ASPIRE, SODA, AUX, and AUGMENT-those structure auditory understanding across time-frequency pattern captioning, hierarchical event/scene description, causal explanation, and goal-driven interpretation, respectively. Together, these paradigms provide a roadmap toward more generalizable, explainable, and human-aligned auditory intelligence, and are intended to catalyze a broader discussion of what it means for machines to understand sound.
Abstract (translated)
近期在听觉智能领域的进展已经产生了高性能的声音事件检测（SED）、声学场景分类（ASC）、自动化音频字幕生成（AAC）和音频问答（AQA）系统。然而，这些任务仍然主要局限于表面级别的识别——捕捉发生了什么，但不涉及为什么发生、其含义或如何在具体情境中展开。我提议重新构想听觉智能为一个分层的、情景化的处理过程，涵盖感知、推理和互动。 为了实现这一观点，我提出了四个受认知启发的任务范式：ASPIRE、SODA、AUX 和 AUGMENT，分别结构化了时间-频率模式描述、层级事件/场景描述、因果解释以及目标驱动的解读。这些范式共同为迈向更具泛化性、可解释性和与人类相一致的听觉智能提供了一个路线图，并旨在激发关于机器如何理解声音更广泛的讨论。
URL
https://arxiv.org/abs/2508.07829
