Machine listening in a neonatal intensive care unit

TL;DR

This paper demonstrates a privacy-preserving, machine listening system in a neonatal intensive care unit that uses edge computing and transfer learning to detect hospital sounds with limited labeled data.

Contribution

It introduces a novel acoustic sensor design for privacy, combined with spectral transcoding and transfer learning for efficient sound event detection in hospitals.

Findings

Detected events align with badge data, confirming accuracy.

System preserves privacy by processing data on-device.

Feasibility of polyphonic listening in sensitive environments.

Abstract

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.