Hybrid Underwater Acoustic Signal Multi-Target Recognition Based on DenseNet-LSTM with Attention Mechanism

Mingchao Zhu; Xiaofeng Zhang; Yansong Jiang; Kejun Wang; Binghua Su; Tenghui Wang

doi:10.1007/978-981-99-6187-0_72

ScienceGate Book Chapters

BOOK-CHAPTER

Hybrid Underwater Acoustic Signal Multi-Target Recognition Based on DenseNet-LSTM with Attention Mechanism

Mingchao Zhu Xiaofeng Zhang Yansong Jiang Kejun Wang Binghua Su Tenghui Wang

Year: 2023 Lecture notes in electrical engineering Pages: 728-738 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-99-6187-0_72

Get Full-Text PDF Get Analytical Report

Keywords:

Underwater Computer science Convolutional neural network Speech recognition SIGNAL (programming language) Artificial neural network Artificial intelligence Underwater acoustic communication Noise (video) Pattern recognition (psychology) Frequency domain Computer vision Geology

Metrics

Cited By

5.53

FWCI (Field Weighted Citation Impact)

Refs

0.95

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Underwater Acoustics Research

Physical Sciences → Earth and Planetary Sciences → Oceanography

Speech and Audio Processing

Physical Sciences → Computer Science → Signal Processing

Gait Recognition and Analysis

Physical Sciences → Engineering → Biomedical Engineering

Hybrid Underwater Acoustic Signal Multi-Target Recognition Based on DenseNet-LSTM with Attention Mechanism

Metrics

Citation History

Topics

Related Documents

Underwater acoustic multi-target recognition based on channel attention mechanism

Underwater Acoustic Target Recognition Based on Multi-scale Residuals and Dual Attention Mechanism

Adaptive underwater acoustic target recognition based on multi-scale residual and attention mechanism

Underwater Acoustic Target Recognition Combining Multi-scale Features and Attention Mechanism

Research on Underwater Acoustic Target Recognition Method Based on DenseNet