基于迁移学习的水声目标识别
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国家自然科学基金(61671156)


Transfer Learning for Acoustic Target Recognition
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    摘要:

    海洋声场环境的时变空变特性、水声目标发声机理的多源性以及其他噪声源的干扰,给水声目标的检测和识别带来很多困难.常规的目标识别手段主要是基于音频时频域特征分析,在复杂海洋环境下的难以获取有效的表征特征及鲁棒的识别效果.为了解决这些问题,本文提出了基于迁移学习的水声目标识别,分别利用预训练网络VGG和VGGish提取深层声学特征及模型微调,实现水声目标的分类识别.实验表明,本文提出的识别算法有效提升了识别准确率,减少了训练时间,基于微调的迁移学习算法在水声目标识别上平均准确率为92.48%,取得了当前最好的识别结果.

    Abstract:

    The time-varying and space-varying characteristics of the marine sound field environment, the multi-source nature of the sound mechanism of underwater acoustic targets, and interference from other noise sources have brought many difficulties to the detection and identification of acoustic targets. Conventional target recognition methods are mainly based on the audio time-frequency domain analysis, it is difficult to obtain effective features and robust recognition effects. In order to solve these problems, transfer learning based acoustic target recognition is proposed. The pre-trained networks VGG and VGGish are used to extract deep acoustic feature analysis and fine-tune respectively. Experiments show that the proposed algorithm effectively improves the recognition accuracy and reduces the training time. The fine-tuned transfer learning algorithm has an average accuracy rate of 92.48% in acoustic target recognition, which achieved the state-of-the-art recognition result.

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邓晋,潘安迪,肖川,刘姗琪.基于迁移学习的水声目标识别.计算机系统应用,2020,29(10):255-261

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  • 收稿日期:2019-12-21
  • 最后修改日期:2020-01-19
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  • 在线发布日期: 2020-09-30
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