让智能耳机听清环境声音，主动识别关键声响。
Enhancing Situational Awareness in Wearable Audio Devices Using a Lightweight Sound Event Localization and Detection System
- 用轻量级环境分类模型引导声音定位检测
- 在模拟数据上定位准确率显著优于传统方法
- 适合需要安全感知的可穿戴音频设备
带有主动降噪（ANC）的可穿戴音频设备虽提升听觉舒适度，却可能掩盖重要环境声，带来安全隐患。为此，我们提出一种环境智能框架，结合声学场景分类（ASC）与声音事件定位与检测（SELD）。系统首先通过轻量级ASC模型判断当前环境，再根据场景预测动态调整SELD网络的敏感度，以检测和定位对当前情境最显著的声音。在模拟耳机数据上，所提出的ASC条件化SELD系统展现出优于传统基线的空间智能。该研究为打造能主动传递环境信息的智能耳戴设备迈出关键一步，有助于实现更安全、更具情境感知能力的听觉体验。
原文摘要 · Abstract (English)
Wearable audio devices with active noise control (ANC) enhance listening comfort but often at the expense of situational awareness. However, this auditory isolation may mask crucial environmental cues, posing significant safety risks. To address this, we propose an environmental intelligence framework that combines Acoustic Scene Classification (ASC) with Sound Event Localization and Detection (SELD). Our system first employs a lightweight ASC model to infer the current environment. The scene prediction then dynamically conditions a SELD network, tuning its sensitivity to detect and localize sounds that are most salient to the current context. On simulated headphone data, the proposed ASC-conditioned SELD system demonstrates improved spatial intelligence over a conventional baseline. This work represents a crucial step towards creating intelligent hearables that can deliver crucial environmental information, fostering a safer and more context-aware listening experience.
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