Pre-processing techniques for improved detection of vocalization sounds in a neonatal intensive care unit
Por:
Raboshchuk G, Nadeu C, Vidiella S, Ros O, Muñoz-Mahamud B and Riverola-de Veciana A
Publicada:
1 ene 2018
Resumen:
The sounds occurring in the noisy acoustical environment of a Neonatal
Intensive Care Unit (NICU) are thought to affect the growth and
neurodevelopment of preterm infants. Automatic sound detection in a NICU
is a novel and challenging problem, and it is an essential step in the
investigation of how preterm infants react to auditory stimuli of the
NICU environment. In this paper, we present our work on an automatic
system for detection of vocalization sounds, which are extensively
present in NICUs. The proposed system reduces the presence of irrelevant
sounds prior to detection. Several pre-processing techniques are
compared, which are based on either spectral subtraction or non-negative
matrix factorization, or a combination of both. The vocalization sounds
are detected from the enhanced audio signal using either generative or
discriminative classification models. An audio database acquired in a
real-world NICU environment is used to assess the performance of the
detection system in terms of frame-level missing and false alarm rates.
The inclusion of the enhancement pre-processing step leads to up to
17.54% relative improvement over the baseline. (C) 2017 Elsevier Ltd.
All rights reserved.
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