Semantic Inference of Bird Songs Using Dynamic Bayesian Networks
Abstract
Knowledge representation and natural language processing are core interests to the field of artificial intelligence (AI). While most research has been directed toward machines and humans, the principles and methods developed for AI might be extended to other species as well. Birds frequently behave in a manner that is intelligent and convey information in their vocalizations that is meaningful to others. In this paper we report on a method combining clustering and dynamic Bayesian networks to describe the semantics of songs among Cassin’s Vireos (Vireo cassinii), and show how behavioral contexts possibly affect bird song output.
Cite
Text
Daimon et al. "Semantic Inference of Bird Songs Using Dynamic Bayesian Networks." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11073Markdown
[Daimon et al. "Semantic Inference of Bird Songs Using Dynamic Bayesian Networks." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/daimon2017aaai-semantic/) doi:10.1609/AAAI.V31I1.11073BibTeX
@inproceedings{daimon2017aaai-semantic,
title = {{Semantic Inference of Bird Songs Using Dynamic Bayesian Networks}},
author = {Daimon, Keisuke and Hedley, Richard W. and Taylor, Charles E.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {4911-4912},
doi = {10.1609/AAAI.V31I1.11073},
url = {https://mlanthology.org/aaai/2017/daimon2017aaai-semantic/}
}