Automated newborn cry diagnostic system using machine learning approach

Author:

Salehian Matikolaie Fatemeh,Kheddache Yasmina,Tadj Chakib

Funder

Bill and Melinda Gates Foundation

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing

Reference61 articles.

1. An evaluation of human perception for neonatal cry using a database of cry and underlying cause;Mukhopadhyay;IEEE,2013

2. Mothers’ and non-mothers’ identification of infant cries;Sagi;Infant Behavior and Development,1981

3. The role of biological rhythms in early psychological development;Wolff;Bull. Menninger Clin.,1967

4. Infant crying: Theoretical and research perspectives;Boukydis,2012

5. ROBUST detection of infant crying in adverse environments using weighted segmental two-dimensional linear frequency cepstral coefficients;Kim;IEEE,2013

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1. An Automated Mood Analysis of Crying Infants Through Sound Recognition Using Hybrid Deep Learning;2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN);2024-07-18

2. Infant cry classification using an efficient graph structure and attention-based model;Kuwait Journal of Science;2024-07

3. Effective infant cry signal analysis and reasoning using IARO based leaky Bi-LSTM model;Computer Speech & Language;2024-06

4. Use of psychoacoustic spectrum warping, decision template fusion, and neighborhood component analysis in newborn cry diagnostic systems;The Journal of the Acoustical Society of America;2024-02-01

5. A Comparative Analysis: Enhancing Baby Cry Detection with Hybrid Deep Learning Techniques;2023 International Conference on Next Generation Electronics (NEleX);2023-12-14

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