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Full-Text Articles in Computer Engineering
Discrimination Of Individual Tigers (Panthera Tigris) From Long Distance Roars, An Ji, Michael T. Johnson, Edward J. Walsh, Joann Mcgee, Douglas L. Armstrong
Discrimination Of Individual Tigers (Panthera Tigris) From Long Distance Roars, An Ji, Michael T. Johnson, Edward J. Walsh, Joann Mcgee, Douglas L. Armstrong
Electrical and Computer Engineering Faculty Research and Publications
This paper investigates the extent of tiger (Panthera tigris) vocal individuality through both qualitative and quantitative approaches using long distance roars from six individual tigers at Omaha's Henry Doorly Zoo in Omaha, NE. The framework for comparison across individuals includes statistical and discriminant function analysis across whole vocalization measures and statistical pattern classification using a hidden Markov model (HMM) with frame-based spectral features comprised of Greenwood frequency cepstral coefficients. Individual discrimination accuracy is evaluated as a function of spectral model complexity, represented by the number of mixtures in the underlying Gaussian mixture model (GMM), and temporal model complexity, …
Ambient Habitat Noise And Vibration At The Georgia Aquarium, Peter M. Scheifele, Michael T. Johnson, Laura W. Kretschmer, John G. Clark, D. Kemper, G. Potty
Ambient Habitat Noise And Vibration At The Georgia Aquarium, Peter M. Scheifele, Michael T. Johnson, Laura W. Kretschmer, John G. Clark, D. Kemper, G. Potty
Electrical and Computer Engineering Faculty Research and Publications
Underwater and in-air noise evaluations were completed in performance pool systems at Georgia Aquarium under normal operating conditions and with performance sound tracks playing. Ambient sound pressure levels at in-pool locations, with corresponding vibration measures from life support system (LSS) pumps, were measured in operating configurations, from shut down to full operation. Results indicate noise levels in the low frequency ranges below 100 Hz were the highest produced by the LSS relative to species hearing thresholds. The LSS had an acoustic impact of about 10 dB at frequencies up to 700 Hz, with a 20 dB re 1 μPa impact …
Acoustic Censusing Using Automatic Vocalization Classification And Identity Recognition, Kuntoro Adi, Michael T. Johnson, Tomasz S. Osiejuk
Acoustic Censusing Using Automatic Vocalization Classification And Identity Recognition, Kuntoro Adi, Michael T. Johnson, Tomasz S. Osiejuk
Electrical and Computer Engineering Faculty Research and Publications
This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden Markovmodels (HMMs) and Gaussian mixture models (GMMs) similar to methods used in the speech recognition community for tasks such as speaker identification and clustering. Initial experiments using a Norwegian ortolan bunting (Emberiza hortulana) data set show the feasibility and effectiveness of the approach. Individually distinct acoustic features have been observed in a wide range of animal …
Automatic Classification And Speaker Identification Of African Elephant (Loxodonta Africana) Vocalizations, Patrick J. Clemins, Michael T. Johnson, Kirsten Leong, Anne Savage
Automatic Classification And Speaker Identification Of African Elephant (Loxodonta Africana) Vocalizations, Patrick J. Clemins, Michael T. Johnson, Kirsten Leong, Anne Savage
Electrical and Computer Engineering Faculty Research and Publications
A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, …