卷积神经网络
计算机科学
水下
洛法尔
特征提取
模式识别(心理学)
人工智能
特征(语言学)
人工神经网络
深度学习
语音识别
电信
低频
地质学
语言学
海洋学
哲学
作者
Xiaobin Yin,Xiaodong Sun,Peishun Liu,Liang Wang,Ruichun Tang
标识
DOI:10.1145/3421766.3421890
摘要
The underwater acoustic target classification task has always been an important research direction of acoustic recognition and classification. The acoustic classification models include traditional models such as Gaussian Mixture Model (GMM), and deep learning models such as Convolutional Neural Network (CNN) and Long and Short Time Memory Network (LSTM). This paper proposes a deep sound feature extraction network based on VGGNet. An underwater acoustic target classification framework based on LOFAR spectrum and CNN is proposed. Although ordinary CNN can also extract underwater acoustic features, too few or too many network layers will cause problems such as insufficient features or increased calculations. Therefore, we draw on the excellent structure of VGGNet in feature extraction and delete several layers for feature extraction and classification of underwater acoustic targets. The accuracy are 94%, 98% and 96% respectively in three real data sets of civil ships, and the accuracy were improved com-pared with the traditional methods.
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