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.

翻译：供氧器、报警装置与脚步声是医院中最常见的声音源。检测这些声音对环境心理学具有科学价值，但其本身面临两大挑战：隐私保护与有限标注数据。本文通过边缘计算与云计算相结合的方式应对这两项挑战。针对隐私保护，我们设计了一种声学传感器，可实时计算三分之一倍频程谱图而非录制音频波形。为实现样本高效的机器学习，我们通过频谱转码与标签空间适配，对预训练音频神经网络（PANN）进行了任务重定位。在新生儿重症监护室开展的小规模研究证实，检测到的事件时间序列与另一种测量模态（即面向家长与医护人员的电子徽章数据）具有一致性。因此，本文论证了在保障隐私设计的前提下，于医院病房实现复调机器听觉的可行性。