4.6 Article

A Robust Feature Extraction Method for Underwater Acoustic Target Recognition Based on Multi-Task Learning

Related references

Note: Only part of the references are listed.
Article Geochemistry & Geophysics

Underwater Acoustic Target Classification Based on Dense Convolutional Neural Network

Van-Sang Doan et al.

Summary: This study proposes an approach using a dense CNN model for underwater target recognition, which can optimize classification rates under complex sound wave propagation characteristics while maintaining low computational cost. Compared to traditional machine learning techniques and other CNN models, this approach achieves high accuracy on a real-world passive sonar dataset.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Environmental Sciences

Imbalanced Underwater Acoustic Target Recognition with Trigonometric Loss and Attention Mechanism Convolutional Network

Yanxin Ma et al.

Summary: The paper introduces a weighted cross entropy loss function based on trigonometric function and applies it in a multi-scale residual convolutional neural network with an attention mechanism to handle class imbalance in underwater acoustic data.

REMOTE SENSING (2022)

Article Engineering, Marine

STM: Spectrogram Transformer Model for Underwater Acoustic Target Recognition

Peng Li et al.

Summary: In this paper, a Transformer-based underwater acoustic target recognition model STM is proposed. It is the first work to introduce Transformer into the underwater acoustic field. Experimental results demonstrate that STM outperforms CNN, ResNet18, and CRNN-9 models in terms of recognition accuracy.

JOURNAL OF MARINE SCIENCE AND ENGINEERING (2022)

Article Acoustics

Integrated neural networks based on feature fusion for underwater target recognition

Qi Zhang et al.

Summary: This paper proposes an integrated neural network method for underwater acoustic target recognition, aiming to enhance recognition accuracy and noise robustness through feature fusion learning. The method extracts multiple features of underwater acoustic signals and uses them as input for training the network, optimizing recognition performance through adjustment of the neural network weight coefficients.

APPLIED ACOUSTICS (2021)

Article Computer Science, Artificial Intelligence

DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification

Muhammad Irfan et al.

Summary: Underwater acoustic classification is a challenging problem due to high background noise and complex sound propagation patterns in the sea environment. Researchers have constructed and presented an underwater acoustic dataset named DeepShip to evaluate existing algorithms and benefit future research. They also conducted a comprehensive study on various machine learning and deep learning algorithms using the dataset.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Acoustics

Deep transfer learning for underwater direction of arrival using one vector sensora)

Huaigang Cao et al.

Summary: A deep transfer learning method is proposed for the direction of arrival estimation using a single-vector sensor, involving CNN training with synthetic source data and adaptation to target domain with at-sea data. By feeding the CNN with acoustic pressure and particle velocity cross-spectra, reliable DOA estimates of a moving surface ship are achieved, outperforming conventional CNNs, especially in the presence of interfering sources.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2021)

Article Multidisciplinary Sciences

Deep convolution stack for waveform in underwater acoustic target recognition

Shengzhao Tian et al.

Summary: In this paper, a novel multiscale residual deep neural network (MSRDN) is proposed for underwater acoustic target recognition, which achieves good recognition accuracy by introducing multiscale residual units (MSRU) to construct the network framework.

SCIENTIFIC REPORTS (2021)

Article Computer Science, Information Systems

An Underwater Acoustic Target Recognition Method Based on Combined Feature With Automatic Coding and Reconstruction

Xinwei Luo et al.

Summary: The paper introduces a target recognition method for underwater acoustic systems based on combined features and automatic encoding. By using an auto-encoder to extract deep data structure, the method is able to achieve better performance compared to traditional methods with the help of data augmentation techniques.

IEEE ACCESS (2021)

Article Acoustics

A multi-task learning convolutional neural network for source localization in deep ocean

Yining Liu et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2020)

Article Engineering, Multidisciplinary

Multi-scale spectral feature extraction for underwater acoustic target recognition

Junjun Jiang et al.

MEASUREMENT (2020)

Article Computer Science, Information Systems

Feature Extraction in Fractional Fourier Domain for Classification of Passive Sonar Signals

Mehdi Shadlou Jahromi et al.

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (2019)

Article Acoustics

Deep convolutional network for animal sound classification and source attribution using dual audio recordings

Tuomas Oikarinen et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2019)

Article Engineering, Electrical & Electronic

Convolutional Neural Network With Second-Order Pooling for Underwater Target Classification

Xu Cao et al.

IEEE SENSORS JOURNAL (2019)

Article Acoustics

Deep-learning source localization using multi-frequency magnitude-only data

Haiqiang Niu et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Deep Learning based Framework for Underwater Acoustic Signal Recognition and Classification

Hao Wu et al.

PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018) (2018)

Article Acoustics

ShipsEar: An underwater vessel noise database

David Santos-Dominguez et al.

APPLIED ACOUSTICS (2016)

Article Acoustics

A wave structure based method for recognition of marine acoustic target signals

Qingxin Meng et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2015)

Article Acoustics

The classification of underwater acoustic target signals based on wave structure and support vector machine

Qingxin Meng et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2014)

Article Acoustics

Automatic classification of underwater targets using fuzzy‐cluster‐based wavelet signatures.

Hui Ou et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2013)

Article Engineering, Multidisciplinary

Bark-wavelet Analysis and HilberteHuang Transform for Underwater Target Recognition

Xiang-yang Zeng et al.

DEFENCE TECHNOLOGY (2013)

Article Computer Science, Artificial Intelligence

Separability of ternary codes for sparse designs of error-correcting output codes

Sergio Escalera et al.

PATTERN RECOGNITION LETTERS (2009)