在新生儿病房实现隐私保护的多声源监听，精准识别医疗设备与脚步声。
Machine listening in a neonatal intensive care unit
- 边缘计算实时生成频谱图，不存储原始音频，保障隐私。
- 用预训练模型迁移学习，仅需少量标注数据即可高效识别声音事件。
- 验证结果与人员定位数据一致，适合医院环境监测场景。
医院中氧气机、报警装置和脚步声是最常见的声音来源，其检测对环境心理学具有科学价值，但面临隐私保护与标注数据稀缺的挑战。本文通过边缘与云端协同计算解决上述问题：设计一种声学传感器，在本地实时计算三倍频程频谱图，不记录音频波形以保障隐私；采用谱域转换与标签空间适配方法，复用预训练音频模型（PANN），实现样本高效机器学习。在新生儿重症监护室（NICU）的小规模研究中，检测到的声音事件时间序列与医护人员及家长佩戴电子徽章的记录高度吻合。结果表明，该系统可在保障隐私的前提下，实现医院病房内多声源的可靠监听。
原文摘要 · Abstract (English)
Oxygenators, alarm devices, and footsteps are some of the most common sound sources in a hospital. Detecting them has scientific value for environmental psychology but comes with challenges of its own: namely, privacy preservation and limited labeled data. In this paper, we address these two challenges via a combination of edge computing and cloud computing. For privacy preservation, we have designed an acoustic sensor which computes third-octave spectrograms on the fly instead of recording audio waveforms. For sample-efficient machine learning, we have repurposed a pretrained audio neural network (PANN) via spectral transcoding and label space adaptation. A small-scale study in a neonatological intensive care unit (NICU) confirms that the time series of detected events align with another modality of measurement: i.e., electronic badges for parents and healthcare professionals. Hence, this paper demonstrates the feasibility of polyphonic machine listening in a hospital ward while guaranteeing privacy by design.
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