Fast ship radiated noise recognition using three-dimensional mel-spectrograms with an additive attention based transformer

Abstract

Passive recognition of ship-radiated noise plays a crucial role in military and economic domains. However, underwater environments pose significant challenges due to inherent noise, reverberation, and time-varying acoustic channels. This paper introduces a novel approach for ship target recognition and classification by leveraging the power of three-dimensional (3D) Mel-spectrograms and an additive attention based Transformer (ADDTr). The proposed method utilizes 3D Mel-spectrograms to capture the temporal variations in both target signal and ambient noise, thereby enhancing both categories’ distinguishable characteristics. By incorporating an additional spatial dimension, the modeling of reverberation effects becomes possible. Through analysis of spatial patterns and changes within the spectrograms, distortions caused by reverberation can be estimated and compensated, so that the clarity of the target signals can be improved. The proposed ADDTr leverages an additive attention mechanism to focus on informative acoustic features while suppressing the influence of noisy or distorted components. This attention-based approach not only enhances the discriminative power of the model but also accelerates the recognition process. It efficiently captures both temporal and spatial dependencies, enabling accurate analysis of complex acoustic signals and precise predictions. Comprehensive comparisons with state-of-the-art acoustic target recognition models on the ShipsEar dataset demonstrate the superiority of the proposed ADDTr approach. Achieving an accuracy of 96.82% with the lowest computation costs, ADDTr outperforms other models.

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

“…To better separate and recover the original shipradiated noise before distortion and mixing, an end-to-end nonlinear BSS network based on an attention mechanism is proposed in this paper. Due to the fact that the Transformer has a shortcoming in capturing local self-dependency and performs well in learning long-term or global dependencies, while convolutional neural networks (CNN) and RNN behave in the opposite way [52][53][54][55][56], an end-to-end network is utilized combining an RNN and multi-head self-attention, i.e., recurrent attention neural networks (RANN). The recurrent attention mechanism is used in image aesthetics, target detection, flow forecasting and time series forecasting [57][58][59][60][61], but it has not been used in nonlinear BSS yet.…”