Machine learning-based classification of recreational fishing vessel kinematics from broadband striation patterns

Abstract

Machine learning is applied to the classification of underwater noise for rapid identification of surface vessel opening and closing behavior. The classification feature employed is the broadband striation pattern observed in a vessel's acoustic spectrogram measured at a nearby hydrophone. Convolutional neural networks are particularly well-suited to the recognition of textures such as interference patterns in broadband noise radiated from moving vessels. Such patterns are known to encode information related to the motion of its source. Rapid understanding of target kinematics through machine learning can provide powerful and informative cues as to the identity and behavior of a detected surface vessel.