Adam Aboudan
M.S. FinalMay 13, 2013, 10am
ECE Conference Room
ACOUSTIC MONITORING SYSTEM FOR FROG POPULATION ESTIMATION
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 or a newly encountered individual. A temporal call overlap detector is also presented as a pre-processing
tool to eliminate overlapping calls from degrading the learning process. The approach uses Mel-frequency cepstral
coefficients (MFCCs) and multivariate Gaussian models (GMs) 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 the in-situ progressive learning process.
In the third part of the thesis, data, performance and results are discussed in detail. 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 and is therefore capable of providing reasonable estimates of the frog population.
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 or a newly encountered individual. A temporal call overlap detector is also presented as a pre-processing
tool to eliminate overlapping calls from degrading the learning process. The approach uses Mel-frequency cepstral
coefficients (MFCCs) and multivariate Gaussian models (GMs) 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 the in-situ progressive learning process.
In the third part of the thesis, data, performance and results are discussed in detail. 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 and is therefore capable of providing reasonable estimates of the frog population.
Adviser: Mahmood Azimi-Sadjadi
Co-Adviser: N/A
Non-ECE Member: Christopher Peterson, Department of Mathematics
Member 3: Kurt Fristrup, Electrical and Computer Engineering Department
Addional Members: N/A
Co-Adviser: N/A
Non-ECE Member: Christopher Peterson, Department of Mathematics
Member 3: Kurt Fristrup, Electrical and Computer Engineering Department
Addional Members: N/A
Publications:
N/A
N/A
Program of Study:
ECE512
ECE514
ECE520
ECE614
ECE651
ECE699
MATH469
STAT525
ECE512
ECE514
ECE520
ECE614
ECE651
ECE699
MATH469
STAT525