Classification of Baby Cries Using Machine Learning Algorithms

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dc.contributor.authorEkinci, Adem
dc.contributor.authorKüçükkülahlı, Enver
dc.date.accessioned2025-03-24T19:50:14Z
dc.date.available2025-03-24T19:50:14Z
dc.date.issued2023
dc.departmentDüzce Üniversitesi
dc.description.abstractPeople are constantly engaged in communication with each other, and they mostly do so through language. The most effective form of communication for a newborn baby until they acquire this skill is crying. Although baby cries are often perceived as bothersome by adult individuals, they can contain a wealth of information. In this study, our goal is to interpret the information embedded in baby cry audio signals using sound processing methods and classify them using machine learning algorithms. To achieve this objective, we utilized a dataset consisting of baby cry audio signals divided into five distinct classes. Feature extraction operations were applied to the dataset, and performance metrics were measured using classification algorithms. Subsequently, to examine the impact of data augmentation on performance metrics, the data was partitioned into equal segments. The changes in performance metrics were analyzed based on the applied data augmentation technique, and it was determined that the employed method enhanced the classification accuracy.
dc.identifier.endpage26
dc.identifier.issn2149-6137
dc.identifier.issue1
dc.identifier.startpage16
dc.identifier.urihttps://hdl.handle.net/20.500.12684/19621
dc.identifier.volume9
dc.language.isoen
dc.publisherAğrı İbrahim Çeçen University
dc.relation.ispartofEastern Anatolian Journal of Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250324
dc.subjectBaby cries|Machine learning|SVM|Random Forest|MLP|k-NN
dc.titleClassification of Baby Cries Using Machine Learning Algorithms
dc.typeArticle

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