倪俊帅,赵梅,胡长青.基于深度学习的舰船辐射噪声多特征融合分类[J].声学技术,2020,39(3):366~371 |
基于深度学习的舰船辐射噪声多特征融合分类 |
Multi-feature fusion classification of ship radiated noise based on deep learning |
投稿时间:2019-12-15 修订日期:2020-01-16 |
DOI:10.16300/j.cnki.1000-3630.2020.03.019 |
中文关键词: 舰船辐射噪声 特征提取 深度学习 多特征融合 舰船分类 |
英文关键词: ship radiated noise feature extraction deep learning multi-feature classification ship classification |
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中文摘要: |
为了改善分类系统的性能,进一步提高舰船辐射噪声分类的正确率,该文提出了一种基于深度神经网络的多特征融合分类方法。该方法首先提取舰船辐射噪声几种不同的特征,将提取的特征同时用于训练具有多个输入分支的深度神经网络,使网络直接在多种特征参数上进行联合学习,通过神经网络的输入分支和连接层实现特征融合,再对舰船辐射噪声进行分类。为了特征深度学习提取了舰船辐射噪声的频谱特征、梅尔倒谱系数和功率谱特征,并将多特征融合分类方法与在一种特征上进行深度学习分类方法的正确率进行对比。实验结果表明,基于深度学习的多特征融合分类方法可以有效地提高舰船辐射噪声分类的正确率,是一种可行的分类方法。 |
英文摘要: |
In order to improve the performance of classification system and further improve the classification accuracy rate of ship radiated noise, a multi-feature fusion classification method based on deep neural network is proposed in this paper. In this method, several different features of the ship radiated noise are extracted firstly, and the extracted features together are used to train a multiple-input deep neural network, so that the network can jointly learn the feature parameters directly and realize the feature fusion through the input branches and connection layers of the neural network, and then the ship radiated noise is classified. The spectral feature, Mel cepstrum coefficients and power spectral feature of the ship radiated noise are extracted for feature deep learning, and the accuracy rate of multi-feature fusion classification method is compared with that of the classification method on a single feature. The experimental results show that the deep learning based multi-feature fusion classification method can effectively improve the accuracy rate of ship radiated noise classification and it is a feasible classification method. |
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