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Deep learning-based framework for expansion, recognition and classification of underwater acoustic signal
Journal of Experimental & Theoretical Artificial Intelligence ( IF 2.2 ) Pub Date : 2019-08-05 , DOI: 10.1080/0952813x.2019.1647560
Guanghao Jin 1 , Fan Liu 1 , Hao Wu 1 , Qingzeng Song 1
Affiliation  

ABSTRACT Recently, deep learning has developed rapidly and contributed in many fields like the classification in radar and sonar applications. In some special fields like the underwater acoustic signals, the dataset for training may be scarce due to the reason of security or other restrictions, which affects the performance of the deep learning methods as those need a big dataset to ensure high accuracy. Furthermore, the original dataset is in some formats like audio, which makes those methods difficult to capture features, especially in insufficient sample case because of the interference. In this paper, we present a novel framework that applies the LOFAR spectrum for preprocessing to retain key features and utilises Generative Adversarial Networks (GAN) for the expansion of samples to improve the performance classification. Firstly, our framework selects proper preprocessing method based on the evaluation of the spectrum methods. Secondly, our framework revises a GAN to generate samples and built an independent classification network to ensure the quality of those. Finally, our framework applies the existing classification networks to evaluate the performance and selects the best one for real utilisation. The experimental results show that the generated samples have high quality, which can significantly improve the classification accuracy of the neural models.

中文翻译:

基于深度学习的水声信号扩展、识别和分类框架

摘要 近年来,深度学习发展迅速,并在雷达和声纳应用中的分类等许多领域做出了贡献。在水声信号等一些特殊领域,由于安全等原因,训练数据集可能会比较稀缺,这影响了深度学习方法的性能,因为这些方法需要大数据集来确保高精度。此外,原始数据集是音频等格式,这使得这些方法难以捕获特征,尤其是在样本不足的情况下,由于干扰。在本文中,我们提出了一种新颖的框架,该框架应用 LOFAR 谱进行预处理以保留关键特征,并利用生成对抗网络 (GAN) 扩展样本以提高性能分类。首先,我们的框架基于对频谱方法的评估选择合适的预处理方法。其次,我们的框架修改了一个 GAN 来生成样本,并构建了一个独立的分类网络来保证样本的质量。最后,我们的框架应用现有的分类网络来评估性能并选择最佳的用于实际利用。实验结果表明,生成的样本具有较高的质量,可以显着提高神经模型的分类精度。我们的框架应用现有的分类网络来评估性能并选择最佳的用于实际利用。实验结果表明,生成的样本具有较高的质量,可以显着提高神经模型的分类精度。我们的框架应用现有的分类网络来评估性能并选择最佳的用于实际利用。实验结果表明,生成的样本具有较高的质量,可以显着提高神经模型的分类精度。
更新日期:2019-08-05
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