Individual Ship Detection Using Underwater Acoustics
Damianos G. Karakos, Jan Silovský, Richard M. Schwartz, William Hartmann, John Makhoul
Abstract
Individual ship detection from underwater audio is the task of deciding whether a specific ship is present, using sound captured by an underwater hydrophone. It is a task analogous to speaker identification (SID), in the sense that it is an open-class detection task; the ships present could be other irrelevant (“impostor”) ships, never encountered in the training data. We present two methodologies for tackling this problem, both motivated by our work in speech-related technologies: (i) one based on neural networks, which follows, to a large extent, the approach of [1], and (ii) one based on i-vectors and PLDA [2]. To the best of our knowledge, this is the first time that the topic of individual ship detection is approached as an open-class detection problem.
BibTeX
@inproceedings{icassp2018_individualshipde,
title = {Individual Ship Detection Using Underwater Acoustics},
author = {Damianos G. Karakos and Jan Silovský and Richard M. Schwartz and William Hartmann and John Makhoul},
booktitle = {ICASSP 2018},
year = {2018}
}