文章摘要
吴承希,王彪,徐千驰,朱雨男.基于小波包分解和PCA-Attention-LSTM的舰船辐射噪声识别技术[J].声学技术,2022,41(2):264~273
基于小波包分解和PCA-Attention-LSTM的舰船辐射噪声识别技术
Ship radiated noise recognition technology based on wavelet packet decomposition and PCA-Attention-LSTM
投稿时间:2020-11-23  修订日期:2021-01-07
DOI:10.16300/j.cnki.1000-3630.2022.02.018
中文关键词: 舰船辐射噪声  小波包分解  特征提取  主成分分析  舰船识别分类
英文关键词: ship radiated noise  wavelet packet decomposition  feature extraction  principal component analysis  ship recognition and classification
基金项目:国家自然科学基金(52071164)资助项目、江苏省研究生科研与实践创新计划项目(KYCX21_3505)
作者单位E-mail
吴承希 江苏科技大学电子信息学院, 江苏镇江 212100 192030054@stu.just.edu.cn 
王彪 江苏科技大学电子信息学院, 江苏镇江 212100  
徐千驰 江苏科技大学电子信息学院, 江苏镇江 212100  
朱雨男 江苏科技大学电子信息学院, 江苏镇江 212100  
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中文摘要:
      为了改进舰船辐射噪声分类系统的性能,进一步提高识别准确率,文章提出了一种基于多特征的小波包分解在长短期记忆(Long Short-Term Memory,LSTM)网络中分类的方法。该方法首先通过小波包分解技术,分频段提取舰船辐射噪声的多种特征,将提取的特征利用主成分分析法(Principal Component Analysis,PCA)进行数据降维,通过添加注意力机制(Attention Mechanism)算法的LSTM网络,对辐射噪声结果分类,提高了学习效率和识别准确率。为了更精细地提取特征,分频段提取了舰船辐射噪声的时频域特征、小波变换特征和梅尔倒谱系数等特征,并将分频段与不分频段的特征、多特征与单一特征、不同信噪比间的算法性能进行对比。实验结果表明,基于小波包分解和PCA-Attention-LSTM的模型可以有效地提高舰船辐射噪声分类的性能,是一种可行的分类方法。
英文摘要:
      In order to improve the performance of the ship radiated noise classification system and further improve the recognition accuracy, a method based on wavelet packet decomposition combined with multi-feature extraction in the long short-term memory (LSTM) network is proposed in this paper. This method first uses wavelet packet decomposition to extract multiple features of ship radiated noise in different frequency bands, and uses principal component analysis (PCA) for data reduction of the extracted features. By the LSTM network added with the attention mechanism algorithm the learning efficiency and recognition accuracy for radiated noise classification are improved. In order to extract the features precisely, the features in time-frequency domain and the features of wavelet transform and Mel-frequency cepstral coefficients (MFCC) of ship radiated noise are extracted in different frequency bands. Then, the performances of the algorithm for features with and without frequency band partition, multi-features and single feature, and different signal to noise ratios are compared. The experimental results show that the model based on wavelet packet decomposition and PCA-Attention-LSTM can effectively improve the performance of ship radiated noise classification and it is a feasible classification method.
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