Underwater sound classification has been an area of interest in the research community
because of its applications in military, commercial, and environmental domains. Underwater …

To improve the recognition accuracy of underwater acoustic targets by artificial neural
network, this study presents a new recognition method that integrates a one-dimensional …

The utilization of machine learning in analyzing ship radiated noise (SR-N) is undergoing
rapid evolution. Because the omnipresent background noise strongly depends on the highly …

In this article, we propose an active target detector by using a shallow neural network (NN)
with novel features under small sonar data, where deep learning (DL) models are restricted …

In the realms of the ocean, it becomes a formidable task to detect and classify the passive
acoustic targets from the convoluted acoustic mixture confronted by the sonar frontend …

Variational autoencoders (VAEs) have emerged as powerful deep generative models for
learning abstract representations in the latent space, making them highly applicable across …

This paper presents a novel clustering approach that utilizes variational autoencoders
(VAEs) with disentangled representations, enhancing the efficiency and effectiveness of …

Abstract Mel-Frequency Cepstral Coefficients (MFCC) and its differential coefficients are
widely used as a typical non-linear spectral envelope feature in passive sonar target …

Multibeam imaging sonar is one of the primary sensors for underwater navigation with
uncrewed underwater vehicles (UUVs) due to the robustness to turbidity and variable …

As a deep generative model, the variational autoencoder (VAE) is widely applied to solve
problems of insufficient samples and imbalanced labels. In the VAE, the distribution of latent …