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<p>Because of complex marine environments and scarce data, underwater acoustic target classification (UATC) is challenging. To improve model generalization ability, data augmentation methods, particularly data synthesis methods based on generative adversarial networks (GANs), are widely adopted. However, the training of GANs is usually slow and unstable. To address these issues, this paper proposes the adaptive stable deep convolutional GAN (AS-DCGAN). We introduce an adaptive controller that controls the learning progress based on the network training performance, thereby avoiding redundant training and accelerating the process. Additionally, we propose a progressive learning strategy that forces the network to gradually learn from low to high frequencies, stabilizing the training. We evaluated AS-DCGAN on two public datasets. The results show that our proposed method achieves state-of-the-art performance, with an accuracy of 81.14% on the DeepShip dataset and 86.11% on the ShipsEar dataset. Therefore, data augmentation and multimodel fusion methods can generate higher-quality data and effectively enhance the performance of UATC classification models.</p>

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Stabilizing underwater acoustic data generation with generative adversarial network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

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  • Yaohui Lyu,
  • Yifei Du,
  • Shengyu Tang,
  • Li Hu

摘要

Because of complex marine environments and scarce data, underwater acoustic target classification (UATC) is challenging. To improve model generalization ability, data augmentation methods, particularly data synthesis methods based on generative adversarial networks (GANs), are widely adopted. However, the training of GANs is usually slow and unstable. To address these issues, this paper proposes the adaptive stable deep convolutional GAN (AS-DCGAN). We introduce an adaptive controller that controls the learning progress based on the network training performance, thereby avoiding redundant training and accelerating the process. Additionally, we propose a progressive learning strategy that forces the network to gradually learn from low to high frequencies, stabilizing the training. We evaluated AS-DCGAN on two public datasets. The results show that our proposed method achieves state-of-the-art performance, with an accuracy of 81.14% on the DeepShip dataset and 86.11% on the ShipsEar dataset. Therefore, data augmentation and multimodel fusion methods can generate higher-quality data and effectively enhance the performance of UATC classification models.