STM: Spectrogram Transformer Model for Underwater Acoustic Target Recognition Article Swipe

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Peng Li , Ji Wu , Yongxian Wang , Qiang Lan , Wenbin Xiao ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.3390/jmse10101428

With the evolution of machine learning and deep learning, more and more researchers have utilized these methods in the field of underwater acoustic target recognition. In these studies, convolutional neural networks (CNNs) are the main components of recognition models. In recent years, a neural network model Transformer that uses a self-attention mechanism was proposed and achieved good performance in deep learning. In this paper, we propose a Transformer-based underwater acoustic target recognition model STM. To the best of our knowledge, this is the first work to introduce Transformer into the underwater acoustic field. We compared the performance of STM with CNN, ResNet18, and other multi-class algorithm models. Experimental results illustrate that under two commonly used dataset partitioning methods, STM achieves 97.7% and 89.9% recognition accuracy, respectively, which are 13.7% and 50% higher than the CNN Model. STM also outperforms the state-of-the-art model CRNN-9 by 3.1% and ResNet18 by 1.8%.

Related Topics

Underwater Acoustics

Transformer

Convolutional Neural Network

Computer Science

Spectrogram

Deep Learning

Artificial Intelligence

Voltage

Engineering

Electrical Engineering

Oceanography

Concepts

Underwater Transformer Convolutional neural network Computer science Spectrogram Deep learning Artificial intelligence Artificial neural network Speech recognition Pattern recognition (psychology) Voltage Engineering Geology Electrical engineering Oceanography

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/jmse10101428
PDF: https://www.mdpi.com/2077-1312/10/10/1428/pdf?version=1666583004
OA Status: gold
Cited By: 45
References: 31
Related Works: 10
OpenAlex ID: https://openalex.org/W4303712167

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DOI: https://doi.org/10.3390/jmse10101428

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Title: STM: Spectrogram Transformer Model for Underwater Acoustic Target Recognition

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-10-04

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Authors: Peng Li, Ji Wu, Yongxian Wang, Qiang Lan, Wenbin Xiao

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Landing page: https://doi.org/10.3390/jmse10101428

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Concepts: Underwater, Transformer, Convolutional neural network, Computer science, Spectrogram, Deep learning, Artificial intelligence, Artificial neural network, Speech recognition, Pattern recognition (psychology), Voltage, Engineering, Geology, Electrical engineering, Oceanography

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Cited by: 45

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Citations by year (recent): 2025: 16, 2024: 19, 2023: 10

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