基于深度森林模型的小尺度水声目标识别

Yafen Dong, Xiaohong Shen, Yongsheng Yan, Haiyan Wang
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引用次数: 1

摘要

水声目标识别是一个备受关注的问题,其关键在于有效的特征提取。目前,由于水声信号处理技术和机器学习的快速发展,水声目标识别领域取得了一定的进展。然而,传统的机器学习方法利用的是浅特征,识别能力有待进一步提高。基于神经网络的深度学习方法虽然可以提取深度特征,但在水下小尺度数据场景下容易出现过拟合等不良现象。这就意味着我们需要找到一种能够提取深层特征的水声目标识别方法,并且要适合于小尺度数据场景。本研究针对上述需求,提出了一种基于深度森林模型的水声目标识别方法。该方法分别采用MFCC特征和深度森林模型作为输入特征向量和分类器。在ShipsEar数据库上的实验结果表明,该方法取得了满意的效果,在小尺度数据水声目标识别领域具有广阔的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small-scale Data Underwater Acoustic Target Recognition with Deep Forest Model
Underwater acoustic target recognition is an issue of great interest, and its key lies in effective feature extraction. Nowadays, due to the rapid development of underwater acoustic signal processing technology and machine learning, some progress has been made in the field of underwater acoustic target recognition. However, traditional machine learning methods utilize shallow features, and the recognition ability needs to be further improved. Although neural network-based deep learning methods can extract deep features, they are prone to over-fitting and other undesirable phenomena in underwater small-scale data scenarios. This means that we need to find a method of underwater acoustic target recognition that can extract deep features, and it should be suitable for small-scale data scenarios. In this research, a method of underwater acoustic target recognition based on the deep forest model is come up with to meet the above requirements. This method adopts MFCC features and the deep forest model as the input feature vectors and classifier, respectively. Experimental results on the ShipsEar database show that the proposed method achieves satisfactory performance and has a promising application in the field of small-scale data underwater acoustic target recognition.
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