Publication:
Abnormal event detection in indoor environment based on acoustic signal processing

dc.citedby3
dc.contributor.authorAbdrakhmanov R.en_US
dc.contributor.authorTolep A.en_US
dc.contributor.authorKozhamkulova Z.en_US
dc.contributor.authorNarbekov N.en_US
dc.contributor.authorDossanov N.en_US
dc.contributor.authorYeskarayeva B.en_US
dc.contributor.authorid57222085447en_US
dc.contributor.authorid57133046300en_US
dc.contributor.authorid57224359860en_US
dc.contributor.authorid57224366230en_US
dc.contributor.authorid57224352870en_US
dc.contributor.authorid57133026800en_US
dc.date.accessioned2023-05-29T09:07:36Z
dc.date.available2023-05-29T09:07:36Z
dc.date.issued2021
dc.description.abstractAlert the public about emergencies is to bring to public alerts and emergency information on dangers arising from the threat or occurrence of emergency situations of natural and technogenic character, as well as the conduct of hostilities or owing to these actions, the rules of behavior of the population and the need for protection activities. The aim of the work is to develop a method for detecting the sounds of critical situations in the sound stream. In this paper, the term "critical situation" is understood as an event, the characteristic sound signs of which can speak of acoustic artifacts (a shot, a scream, a glass strike, an explosion, a siren, etc.). The developed method allows you to classify events into two groups: Normal (for example, street noise) and critical situations (for example, an explosion, a scream, a shot). To determine events, machine learning is used, namely the Support Vector Machine method, which solves classification and regression problems by constructing a nonlinear plane separating the solutions. SVM has a fairly wide application in data classification and shows good results in event detection problems. As part of the work, the minimum set of features for the machine learning model was determined, small training and test samples were formed, and a method was developed that classifies normal and abnormal events. � 2021 Little Lion Scientific. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage2205
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85107452121
dc.identifier.spage2192
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85107452121&partnerID=40&md5=a02b1406bd33b0ef7b62c1edbc2d5e7f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26192
dc.identifier.volume99
dc.publisherLittle Lion Scientificen_US
dc.sourceScopus
dc.sourcetitleJournal of Theoretical and Applied Information Technology
dc.titleAbnormal event detection in indoor environment based on acoustic signal processingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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