DSCANet:利用深度可分离卷积注意力模块进行水下声学目标分类

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Chonghua Tang, Gang Hu
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引用次数: 0

摘要

水下目标分类和识别技术对于支持水下声学信息对抗措施至关重要。研究重点是水下目标特征的提取和分类。研究人员从多个角度进行了深入研究。由于环境噪声和不同目标的各种工作条件的影响,水下声学信号的信噪比普遍较低。此外,这些信号的成分复杂,通常需要特定的信号预处理技术,如信号增强和分解。在目前的方法中,主要侧重于在多步骤预处理后提取水下声学信号的特征并对其进行分类。然而,这些方法并没有有效整合特征提取和分类。为了解决这些局限性,我们提出了一种名为深度可分离卷积注意(DSCA)的新模型,并使用多个 DSCA 实例来构建神经网络,我们称之为 DSCANet。DSCANet 集成了水下声学目标的特征提取和目标分类。我们工作中的 "目标 "指的是水下声源。DSCANet 的结构统一而简单,无需对水下声学信号进行特定的预处理。DSCANet 在开放数据集 ShipsEars 上进行了训练和验证。它的分类准确率达到 93%,是对比实验中最高的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

DSCANet: underwater acoustic target classification using the depthwise separable convolutional attention module

DSCANet: underwater acoustic target classification using the depthwise separable convolutional attention module

The technology for classifying and recognizing underwater targets is crucial for supporting underwater acoustic information countermeasures. The research focus is on the extraction and classification of features of underwater targets. Researchers have conducted an in-depth study from various perspectives. Due to the influence of ambient noise and various operating conditions of different targets, the signal-to-noise ratio of underwater acoustic signals is generally meager. Additionally, the components of these signals are complex, often requiring specific signal pre-processing techniques such as signal enhancement and decomposition. In current methods, there is a primary focus on extracting and classifying features of underwater acoustic signals after multi-step preprocessing. However, these methods do not effectively integrate feature extraction and classification. To address these limitations, we propose a new model called Depthwise Separable Convolutional Attention (DSCA) and use multiple instances of DSCA to construct a neural network, which we call DSCANet. The DSCANet integrates feature extraction and target classification for underwater acoustic targets. The ’target’ in our work should be mentioned as it refers to underwater sources of sound. The structure of DSCANet is unified and simple, and no specific pre-processing of the underwater acoustic signal is necessary. The DSCANet is trained and validated on ShipsEars, an open dataset. It achieves a classification accuracy of 93%, which is the highest in the contrast experiment.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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