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时频谱图和数据增强的水声信号深度学习目标识别方法*

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刘峰,罗再磊,沈同圣,赵德鑫.时频谱图和数据增强的水声信号深度学习目标识别方法*[J].,2021,40(4):518-524
时频谱图和数据增强的水声信号深度学习目标识别方法*
Deep learning target recognition method of underwater acoustic signal based on data augmentation and time-frequency spectrum
投稿时间：2020-09-27修订日期：2021-07-07
中文摘要:
水声目标识别一直是水声领域研究的重点问题之一，深度学习方法可以有效地解决目标识别问题，然而，水声样本的稀少限制了该方法的应用。该文 提出一种基于数据增强的水声信号深度学习目标识别方法，该方法以Mel功率谱作为网络的输入特征，通过对原始信号在时域和时频域的拉伸和掩蔽等变换，实现数据扩展和增加泛化性能的目的，最后，利用改进的VGG网络模型实现目标分类。实验结果表明，该文方法得到的水下目标识别准确率(95.2%) 要优于其他4种对比方法，证明了该文提出的网络模型和数据增强方法均有助于提高目标分类性能。
英文摘要:
Underwater acoustic target recognition has always been one of the key issues in the field of underwater acoustic research. Deep learning methods can effectively solve the problem of target recognition. However, the scarcity of underwater acoustic samples limits the application of this method. This paper proposes a deep learning target recognition method for underwater acoustic signals based on data enhancement. This method uses Mel power spectrum as the input feature of the network, and in order to increase the generalization performance of the method, data augmentation is achieved by realizes the data by stretching and masking the original signal in the time domain and time-frequency domain. The purpose of extending and increasing generalization performance, and Ffinally, using an improved VGG network model to achieve target classification. The experimental results show that the underwater target recognition accuracy (95.2%) obtained by this method is better than the other four comparison methods, which demonstrates that the network model and data enhancement method proposed in this paper can help to improve the target classification performance.
DOI：10.11684/j.issn.1000-310X.2021.04.004
中文关键词:水声目标识别，卷积神经网络，数据增强，Mel功率谱
英文关键词:Underwateracoustic targetrecognition, convolutionalneural network, dataaugmentation, Melspectrogram
基金项目:(41906169)
作者单位E-mail
刘峰军事科学院 国防科技创新研究院 北京liufeng_cv@126.com
罗再磊军事科学院 国防科技创新研究院 北京
沈同圣军事科学院 国防科技创新研究院 北京shents@126.com
赵德鑫军事科学院 国防科技创新研究院 北京zhaodexin@126.com
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