Individual Ship Detection Using Underwater Acoustics

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.