Machine listening in a neonatal intensive care unit
<|reference_start|>Machine listening in a neonatal intensive care unit: 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.<|reference_end|>
@article{tailleur2024machine,
    title={Machine listening in a neonatal intensive care unit},
    author={Modan Tailleur (LS2N,Nantes Univ - ECN,LS2N - 'equipe SIMS), Vincent
  Lostanlen (LS2N,LS2N - 'equipe SIMS,Nantes Univ - ECN), Jean-Philippe
  Rivi`ere (Nantes Univ,Nantes Univ - UFR FLCE,LS2N,LS2N - 'equipe PACCE),
  Pierre Aumond (UMRAE)},
    journal={Workshop on Detection and Classification of Acoustic Scenes and
  Events (DCASE), Oct 2024, Tokyo, Japan},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11439},
    primaryClass={cs.SD cs.AI cs.LG eess.AS}
}