{"title":"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","authors":"Chonghua Tang, Gang Hu","doi":"10.1007/s12145-024-01479-0","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DSCANet: underwater acoustic target classification using the depthwise separable convolutional attention module\",\"authors\":\"Chonghua Tang, Gang Hu\",\"doi\":\"10.1007/s12145-024-01479-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01479-0\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01479-0","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.