| REVIEW ARTICLE |
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| Year : 2022 | Volume
: 1
| Issue : 2 | Page : 82-87 |
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Interdisciplinary collaboration between engineering and nursing on baby crying analyzing and classification: A biotechnology study
Serap Ozdemir1, Efe Çetin Yilmaz2
1 Department of Nursing, Yusuf Şerefoğlu Faculty of Health Sciences, Kilis 7 Aralık University, Kilis, Turkey
2 Department of Control Systems Electrical and Electronic Engineering, Kilis 7 Aralık University Faculty of Engineering and Architecture, Kilis, Turkey
Correspondence Address:
Dr. Serap Ozdemir
Department of Nursing, Yusuf Şerefoğlu Faculty of Health Sciences, Kilis 7 Aralik University, Kilis
Turkey
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jpdtsm.jpdtsm_14_22
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This study aims to reveal a multidisciplinary study on analysis and signal processing on infant crying in the field of engineering and nursing. It is a known fact that babies report all their needs with crying behavior. It is often very difficult for those responsible for the baby to determine the needs of the baby with this crying behavior. It is of great importance for the comfort of the baby that the parents can accurately predict the crying behavior and needs of the babies. For this reason, the analysis of the sound signals produced by babies during crying behavior is an interesting subject in the field of engineering. In the literature, proposed approaches capture the baby's cry signal and extract a unique set of features from this signal using Mel Frequency Cepstral Coefficients, Linear Predictive Cepstral Coefficients, and pitch. This feature set is used to distinguish between partner signals to recognize the causes of crying. Furthermore, this classification is used to represent different classes of causes of crying, such as hunger, pain, sleep, and discomfort. As a result, in this study, the clinical analysis of infant crying behaviors was examined and optimum solutions were evaluated in terms of engineering. Thus, new approaches have been tried to be brought by analyzing artificial intelligence-based sound analysis systematics.
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