Analyzing 50 kHz rats’ vocalizations using machine learning approaches

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dc.contributor.authorYazdani Sangdeh, Shayan
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorEuston, David R.
dc.date.accessioned2021-12-21T17:31:06Z
dc.date.available2021-12-21T17:31:06Z
dc.date.issued2021
dc.degree.levelMastersen_US
dc.description.abstractRecent studies propose different categorization schemes for rats’ 50 kHz vocalizations. This study attempted to differentiate these categories based on spectrographic features extracted manually and using convolutional neural networks (CNN). In order to analyze the separability of the categories, we trained different classifiers on the extracted features and the best performance was achieved by a support vector machine (SVM) algorithm using the features derived from a CNN which yielded an accuracy of 63.67%. The results showed that many call categories have a high degree of overlap, suggesting that rats may also have a difficult time discriminating them. Next, we created a dendrogram using D-prime scores (a separability measure) generated from our SVM classifier. This dendrogram suggested a new grouping of calls into 7 different categories that are highly dissimilar. Finally, we trained another SVM model on the 7 new categories and achieved 77.8% accuracy.en_US
dc.identifier.urihttps://hdl.handle.net/10133/6116
dc.language.isoen_USen_US
dc.proquest.subject0800en_US
dc.proquest.subject0317en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Neuroscienceen_US
dc.publisher.departmentDepartment of Neuroscienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectraten_US
dc.subjectmachine learningen_US
dc.subjectvocalizationen_US
dc.subjectacousticen_US
dc.subjectclassificationen_US
dc.subjectRats--Vocalization--Researchen_US
dc.subjectRats--Behavior--Researchen_US
dc.subjectAnimal communication--Researchen_US
dc.subjectMachine learningen_US
dc.subjectSupport vector machinesen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectDissertations, Academicen_US
dc.titleAnalyzing 50 kHz rats’ vocalizations using machine learning approachesen_US
dc.typeThesisen_US
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