Classification of Ship Radiated Noise Based on Bi-Logarithmic Scale Spectrum and Convolutional Network
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摘要: 卷积层平移等变性与线性谱不适配，卷积网络对高维特征的长距离依赖建模能力不足。该文提出一种双对数谱特征用于船舶辐射噪声分类。双对数谱通过重新排列对数谱频点，保证高频端分辨率的同时，规避使用太深的卷积网络。利用双对数谱各行表征同一目标的先验知识，构建卷积网络和目标函数。DeepShip数据集上的试验结果表明，特征维数相同情况下，提出的算法分类正确率比以线性谱为输入的卷积网络提高2.4%以上。Abstract: The translation equivariance of convolutional layers are not compatible with the linear spectrum. Therefore, the convolutional networks can not carry the long-distance dependency of high-dimensional features. One bi-logarithmic spectrum feature is presented by this paper for classification of ship radiated noise. This bi-logarithmic spectrum rearranges the frequency points of the logarithmic spectrum to ensure the resolution of high frequencies, therefore the substantial deep convolutional network is not necessary. Considering on the prior knowledge that each row of the bi-logarithmic spectrum corresponding to the same one target, a convolutional network as well as an objective function are constructed. Then this network is trained and tested with DeepShip dataset to classify four types of marine vessels, and the results show that, with the same feature dimensions, the classification accuracy of the algorithm proposed by this paper is improved by 2.4% more than the convolutional network with the input feature of linear scale spectrum.
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表 2 ACNN 的网络结构参数
层号 层 参数 输入$1 \times I \times J$维 1 Conv2d (32, 1×5) 2 Conv2d+MaxPool2d (64, 1×3), 1×2 3 Conv2d+MaxPool2d (64, 1×3), 1×2 4 Conv2d+MaxPool2d (64, 1×3), 1×2 5 Conv2d+MaxPool2d (64, 1×3), 1×2 6 Conv2d+MaxPool2d (128, 1×3), 1×2 变形为$ 4J \times I $维 7 Conv1d (256, 1) 8 Conv1d (64, 1) 9 Conv1d+AvgPool1d (4, 1), $I$ 
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表 3 对照组 CNN 结构参数
网络 输入维数 卷积核数 CNN1d 256 32-64-64-64-64-128 1024 16-32-64-64-64-64-128 2048 8-16-32-64-64-64-64-128 CNN2d 4×256 32-64-64-64-64-128 8×256 全连接层均为1024-256-64-4
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表 4 分类正确率
网络 特征 维数 正确率(%) CNN1d 线性谱 256 63.62 1024 64.70 2048 64.60 对数谱 256 65.02 1024 66.25 2048 65.31 CNN2d 双对数谱 4×256 66.07 ACNN 66.86 ACNN+约束 67.11 CNN2d 8×256 66.35 ACNN 67.32 ACNN+约束 67.47
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