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Received:September 01, 2024 Revised:October 10, 2024
Received:September 01, 2024 Revised:October 10, 2024
中文摘要: 面对复杂的海洋环境, 利用舰船辐射噪声进行水声目标特征提取与识别具有极大的挑战性. 本文首先将船舶音频信号的三维梅尔频率倒谱系数(3D dynamic Mel-frequency cepstrum coefficient, 3D-MFCC)特征与三维梅尔谱(3D dynamic Mel-spectrogram, 3D-Mel)特征进行融合作为模型输入, 并基于此提出了一种新的水声目标识别深度神经网络模型, 该模型在卷积神经网络(convolutional neural network, CNN)和长短期记忆网络(long short-term memory, LSTM)的串行架构基础上, 用多尺度深度可分离卷积网络(multi-scale depthwise convolutional network, MSDC), 替代了传统的CNN, 并增加了多尺度通道注意力机制(multi-scale channel attention, MSCA). 实验结果表明, 该方法在DeepShip数据集和ShipsEar数据集上的平均识别率分别达到了85.92%和97.32%, 展现了良好的分类效果.
中文关键词: 舰船辐射噪声 3D特征融合 多尺度深度可分离卷积 多尺度通道注意力机制
Abstract:Facing the complex marine environment, it is extremely challenging to utilize ship radiated noise for hydroacoustic target feature extraction and recognition. In this study, 3D dynamic Mel-frequency cepstrum coefficient (3D-MFCC) features of ship audio signals are fused with 3D dynamic Mel-spectrogram (3D-Mel) features as model inputs. Based on this, a new deep neural network model for hydroacoustic target recognition is proposed. The model is based on the serial architectures of convolutional neural network (CNN) and long short-term memory (LSTM). Here, the traditional CNN is replaced by multi-scale depthwise convolutional network (MSDC), and multi-scale channel attention (MSCA) is added. The experimental results show that the average recognition rate of this method on DeepShip and ShipsEar datasets reaches 85.92% and 97.32% respectively, which demonstrates a good classification effect.
keywords: ship radiation noise 3D feature fusion multiscale depth-separable convolution multi-scale channel attention mechanism
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基金项目:国家自然科学基金青年科学基金(62203405); 山西省应用基础研究计划(20210302124545, 202303021212206, 202202110401015)
引用文本：
许玮婷,赵英亮,冯思奇,韩星程,贾彩琴.基于3D融合特征联合神经网络的水声目标识别.计算机系统应用,2025,34(3):72-84
XU Wei-Ting,ZHAO Ying-Liang,FENG Si-Qi,HAN Xing-Cheng,JIA Cai-Qin.Hydroacoustic Target Recognition Based on 3D Fusion Feature Joint Neural Network.COMPUTER SYSTEMS APPLICATIONS,2025,34(3):72-84
许玮婷,赵英亮,冯思奇,韩星程,贾彩琴.基于3D融合特征联合神经网络的水声目标识别.计算机系统应用,2025,34(3):72-84
XU Wei-Ting,ZHAO Ying-Liang,FENG Si-Qi,HAN Xing-Cheng,JIA Cai-Qin.Hydroacoustic Target Recognition Based on 3D Fusion Feature Joint Neural Network.COMPUTER SYSTEMS APPLICATIONS,2025,34(3):72-84
