The study of dolphin cognition involves intensive research of animal vocal-
izations recorded in the field. In this dissertation I address the automated analysis
of audible dolphin communication. I propose a system called the signal imager that
automatically discovers patterns in dolphin signals. These patterns are invariant to
frequency shifts and time warping transformations. The discovery algorithm is based
on feature learning and unsupervised time series segmentation using hidden Markov
models. Researchers can inspect the patterns visually and interactively run com-
parative statistics between the distribution of dolphin signals in different behavioral
contexts. The required statistics for the comparison describe dolphin communication
as a combination of the following models: a bag-of-words model, an n-gram model
and an algorithm to learn a set of regular expressions. Furthermore, the system can
use the patterns to automatically tag dolphin signals with behavior annotations. My
results indicate that the signal imager provides meaningful patterns to the marine
biologist and that the comparative statistics are aligned with the biologists’ domain
knowledge.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53968 |
| Date | 21 September 2015 |
| Creators | Kohlsdorf, Daniel |
| Contributors | Starner, Thad |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
Page generated in 0.0019 seconds