Acoustic monitoring system for frog population estimation using in-situ progressive learning
Date
2013
Authors
Aboudan, Adam, author
Azimi-Sadjadi, Mahmood R., advisor
Fristrup, Kurt, committee member
Peterson, Christopher, committee member
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Abstract
Frog 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.
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Subject
Gaussian modeling
MFCC
likelihood function
Kullback-Liebler divergence
individual recognition