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Acoustic monitoring system for frog population estimation using in-situ progressive learning

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dc.contributor.authorAboudan, Adam, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., advisor
dc.contributor.authorFristrup, Kurt, committee member
dc.contributor.authorPeterson, Christopher, committee member
dc.date.accessioned2007-01-03T05:55:01Z
dc.date.available2007-01-03T05:55:01Z
dc.date.issued2013
dc.description.abstractFrog populations are considered excellent bio-indicators and hence the ability to monitor changes in their populations can be very useful for ecological research and environmental monitoring. This thesis presents a new population estimation approach based on the recognition of individual frogs of the same species, namely the Pseudacris Regilla (Pacific Chorus Frog), which does not rely on the availability of prior training data. An in-situ progressive learning algorithm is developed to determine whether an incoming call belongs to a previously detected individual frog or a newly encountered individual frog. A temporal call overlap detector is also presented as a pre-processing tool to eliminate overlapping calls. This is done to prevent the degrading of the learning process. The approach uses Mel-frequency cepstral coefficients (MFCCs) and multivariate Gaussian models to achieve individual frog recognition. In the first part of this thesis, the MFCC as well as the related linear predictive cepstral coefficients (LPCC) acoustic feature extraction processes are reviewed. The Gaussian mixture models (GMM) are also reviewed as an extension to the classical Gaussian modeling used in the proposed approach. In the second part of this thesis, the proposed frog population estimation system is presented and discussed in detail. The proposed system involves several different components including call segmentation, feature extraction, overlap detection, and the in-situ progressive learning process. In the third part of the thesis, data description and system performance results are provided. The process of synthetically generating test sequences of real frog calls, which are applied to the proposed system for performance analysis, is described. Also, the results of the system performance are presented which show that the system is successful in distinguishing individual frogs, hence capable of providing reasonable estimates of the frog population. The system can readily be transitioned for the purpose of actual field studies.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierAboudan_colostate_0053N_11807.pdf
dc.identifier.urihttp://hdl.handle.net/10217/80214
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectGaussian modeling
dc.subjectMFCC
dc.subjectlikelihood function
dc.subjectKullback-Liebler divergence
dc.subjectindividual recognition
dc.titleAcoustic monitoring system for frog population estimation using in-situ progressive learning
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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