Deep Learning-Based Classification of Raw Hydroacoustic Signal: A Review

Abstract

Underwater target recognition is a research component that is crucial to realizing crewless underwater detection missions and has significant prospects in both civil and military applications. This paper provides a comprehensive description of the current stage of deep-learning methods with respect to raw hydroacoustic data classification, focusing mainly on the variety and recognition of vessels and environmental noise from raw hydroacoustic data. This work not only aims to describe the latest research progress in this field but also summarizes three main elements of the current stage of development: feature extraction in the time and frequency domains, data enhancement by neural networks, and feature classification based on deep learning. In this paper, we analyze and discuss the process of hydroacoustic signal processing; demonstrate that the method of feature fusion can be used in the pre-processing stage in classification and recognition algorithms based on raw hydroacoustic data, which can significantly improve target recognition accuracy; show that data enhancement algorithms can be used to improve the efficiency of recognition in complex environments in terms of deep learning network structure; and further discuss the field’s future development directions.

Efficient ship noise classification with positive incentive noise and fused features using a simple convolutional network

“…To address these issues, various signal processing methods have been proposed for extracting features of hydroacoustic signals, including LOFAR spectra, Meier scalar spectrograms, Meier cepstral coefficients (MFCC), and Hilbert-Huang transform features 2 . With the development of deep learning, features based on these methods have been used to develop ship signal identification models 3 – 5 .…”