The study of dolphin cognition involves intensive research of animal vocalizations recorded in the field. We address the automated analysis of audible dolphin communication and propose a system 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 comparative statistics between the distribution of dolphin signals in different behavioral contexts. Our results indicate that our system provides meaningful patterns to the marine biologist and that the comparative statistics are aligned with the biologists domain knowledge.
Cite as: Kohlsdorf, D., Herzing, D., Starner, T. (2016) Feature Learning and Automatic Segmentation for Dolphin Communication Analysis. Proc. Interspeech 2016, 2621-2625, doi: 10.21437/Interspeech.2016-748
@inproceedings{kohlsdorf16_interspeech,
author={Daniel Kohlsdorf and Denise Herzing and Thad Starner},
title={{Feature Learning and Automatic Segmentation for Dolphin Communication Analysis}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={2621--2625},
doi={10.21437/Interspeech.2016-748}
}