Lykartsis, AthanasiosHädrich, MarkusWeinzierl, Stefan2022-05-112022-05-112021-12-08978-9-0827-9706-0https://depositonce.tu-berlin.de/handle/11303/16869http://dx.doi.org/10.14279/depositonce-15647We present a prototype system for the acoustic monitoring of artificially ventilated patients in intensive care. A device placed in the patient room detects sounds indicating an emergency situation and notifies a pager of the care staff. The staff can react more quickly and take appropriate action, as well as provide feedback on the prediction for continual learning. A microphone array with adaptive beamforming and an integrated microcomputer is employed, autonomously performing recording, audio preprocessing as well as deep learning based inference. The training dataset originates from a variety of patients and spatial and sonic environments, accommodating for different patterns of background noise and distortions. Mel spectrograms of short length are extracted and used for training a convolutional neural network. An initial evaluation of the system shows an accuracy of 80% for a binary, balanced dataset. The system is deployed in several intensive care facilities and can easily be adapted to other types of medically relevant sounds.en534 Schall und verwandte Schwingungenacoustic monitoringbeamformingconvolutional neural networkmel spectrogramsConference Object2076-1465