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A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes

The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a ra...

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Autores principales: Abou-Abbas, Lina, Tadj, Chakib, Fersaie, Hesam Alaie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Acoustical Society of America 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593797/
https://www.ncbi.nlm.nih.gov/pubmed/28964073
http://dx.doi.org/10.1121/1.5001491
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author Abou-Abbas, Lina
Tadj, Chakib
Fersaie, Hesam Alaie
author_facet Abou-Abbas, Lina
Tadj, Chakib
Fersaie, Hesam Alaie
author_sort Abou-Abbas, Lina
collection PubMed
description The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for automatic cry sound segmentation for application in a cry-based diagnostic system has been proposed. The contribution of various additional time- and frequency-domain features to increase the robustness of a Gaussian mixture model/hidden Markov model (GMM/HMM)-based cry segmentation system in noisy environments is studied. A fully automated segmentation algorithm to extract cry sound components, namely, audible expiration and inspiration, is introduced and is grounded on two approaches: statistical analysis based on GMMs or HMMs classifiers and a post-processing method based on intensity, zero crossing rate, and fundamental frequency feature extraction. The main focus of this paper is to extend the systems developed in previous works to include a post-processing stage with a set of corrective and enhancing tools to improve the classification performance. This full approach allows to precisely determine the start and end points of the expiratory and inspiratory components of a cry signal, EXP and INSV, respectively, in any given sound signal. Experimental results have indicated the effectiveness of the proposed solution. EXP and INSV detection rates of approximately 94.29% and 92.16%, respectively, were achieved by applying a tenfold cross-validation technique to avoid over-fitting.
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spelling pubmed-55937972017-09-29 A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes Abou-Abbas, Lina Tadj, Chakib Fersaie, Hesam Alaie J Acoust Soc Am Signal Processing in Acoustics The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for automatic cry sound segmentation for application in a cry-based diagnostic system has been proposed. The contribution of various additional time- and frequency-domain features to increase the robustness of a Gaussian mixture model/hidden Markov model (GMM/HMM)-based cry segmentation system in noisy environments is studied. A fully automated segmentation algorithm to extract cry sound components, namely, audible expiration and inspiration, is introduced and is grounded on two approaches: statistical analysis based on GMMs or HMMs classifiers and a post-processing method based on intensity, zero crossing rate, and fundamental frequency feature extraction. The main focus of this paper is to extend the systems developed in previous works to include a post-processing stage with a set of corrective and enhancing tools to improve the classification performance. This full approach allows to precisely determine the start and end points of the expiratory and inspiratory components of a cry signal, EXP and INSV, respectively, in any given sound signal. Experimental results have indicated the effectiveness of the proposed solution. EXP and INSV detection rates of approximately 94.29% and 92.16%, respectively, were achieved by applying a tenfold cross-validation technique to avoid over-fitting. Acoustical Society of America 2017-09 2017-09-11 /pmc/articles/PMC5593797/ /pubmed/28964073 http://dx.doi.org/10.1121/1.5001491 Text en © 2017 Author(s) 0001-4966/2017/142(3)/1318/14 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Signal Processing in Acoustics
Abou-Abbas, Lina
Tadj, Chakib
Fersaie, Hesam Alaie
A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
title A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
title_full A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
title_fullStr A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
title_full_unstemmed A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
title_short A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
title_sort fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes
topic Signal Processing in Acoustics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593797/
https://www.ncbi.nlm.nih.gov/pubmed/28964073
http://dx.doi.org/10.1121/1.5001491
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