Automatic Classification of Animal Vocalizations

This manuscript describes a framework for the
analysis and
classification of animal vocalizations. The
framework combines generalized perceptual linear
prediction (gPLP) features, which incorporate
information about the ability of the species under
study to perceive sounds, and a hidden Markov model
(HMM) classification system. The effectiveness of
the framework is shown by analyzing African elephant and
beluga whale vocalizations. The features extracted
from the African elephant data are used as input to a
supervised classification system and compared to
results from traditional statistical tests. The gPLP
features extracted from the beluga whale data are
used in an unsupervised classification system and the
results are compared to labels assigned by experts.
The development of a framework from which to build
animal vocalization classifiers will provide
bioacoustics researchers with a consistent platform
to analyze and classify vocalizations.

Autorentext

Patrick received his bachelors, masters, and doctorate inElectrical and Computer Engineering from Marquette University,focusing on machine learning and digital signal processing. While working on this dissertation research, he also taughtpart-time and served as President of the alumni board for theMarquette Chapter of Triangle Fraternity.

Klappentext

This manuscript describes a framework for the analysis and classification of animal vocalizations. The framework combines generalized perceptual linear prediction (gPLP) features, which incorporate information about the ability of the species under study to perceive sounds, and a hidden Markov model (HMM) classification system. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations.