A Deep Learning Approach for Distant Infrasound Signals Classification
Infrasound signal classification represents a critical challenge that demands immediate attention. Feature extraction stands as the core concept for enhancing classification accuracy in infrasound signal processing. However, existing feature extraction methodologies fail to meet the requirements for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2058 |
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| author | Xiaofeng Tan Xihai Li Hongru Li Xiaoniu Zeng Tianyou Liu Shengjie Luo |
| author_facet | Xiaofeng Tan Xihai Li Hongru Li Xiaoniu Zeng Tianyou Liu Shengjie Luo |
| author_sort | Xiaofeng Tan |
| collection | DOAJ |
| description | Infrasound signal classification represents a critical challenge that demands immediate attention. Feature extraction stands as the core concept for enhancing classification accuracy in infrasound signal processing. However, existing feature extraction methodologies fail to meet the requirements for long-distance detection scenarios. To address these limitations, this study proposes a novel classification framework based on the spatiotemporal characteristics of infrasound signals. The proposed framework incorporates advanced signal processing techniques, signal enhancement algorithms, and deep learning architectures to achieve precise classification of infrasound signals. This paper designs three sets of comparative experiments, and the results demonstrate that the proposed method achieves a classification accuracy rate of 83.9% on chemical explosion and seismic infrasound datasets, outperforming eight other comparative classification methods. This substantiates the efficacy of the proposed approach. |
| format | Article |
| id | doaj-art-c9ef84e7867c4c0b8a705f0900a7f7f0 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c9ef84e7867c4c0b8a705f0900a7f7f02025-08-20T03:03:25ZengMDPI AGSensors1424-82202025-03-01257205810.3390/s25072058A Deep Learning Approach for Distant Infrasound Signals ClassificationXiaofeng Tan0Xihai Li1Hongru Li2Xiaoniu Zeng3Tianyou Liu4Shengjie Luo5School of nuclear Engineering, Rocket Force University of Engneering, Xi’an 710025, ChinaSchool of nuclear Engineering, Rocket Force University of Engneering, Xi’an 710025, ChinaSchool of nuclear Engineering, Rocket Force University of Engneering, Xi’an 710025, ChinaSchool of nuclear Engineering, Rocket Force University of Engneering, Xi’an 710025, ChinaSchool of nuclear Engineering, Rocket Force University of Engneering, Xi’an 710025, ChinaSchool of nuclear Engineering, Rocket Force University of Engneering, Xi’an 710025, ChinaInfrasound signal classification represents a critical challenge that demands immediate attention. Feature extraction stands as the core concept for enhancing classification accuracy in infrasound signal processing. However, existing feature extraction methodologies fail to meet the requirements for long-distance detection scenarios. To address these limitations, this study proposes a novel classification framework based on the spatiotemporal characteristics of infrasound signals. The proposed framework incorporates advanced signal processing techniques, signal enhancement algorithms, and deep learning architectures to achieve precise classification of infrasound signals. This paper designs three sets of comparative experiments, and the results demonstrate that the proposed method achieves a classification accuracy rate of 83.9% on chemical explosion and seismic infrasound datasets, outperforming eight other comparative classification methods. This substantiates the efficacy of the proposed approach.https://www.mdpi.com/1424-8220/25/7/2058infrasound signal classificationdeep learningnoise reductionstation signal combinationCNN |
| spellingShingle | Xiaofeng Tan Xihai Li Hongru Li Xiaoniu Zeng Tianyou Liu Shengjie Luo A Deep Learning Approach for Distant Infrasound Signals Classification Sensors infrasound signal classification deep learning noise reduction station signal combination CNN |
| title | A Deep Learning Approach for Distant Infrasound Signals Classification |
| title_full | A Deep Learning Approach for Distant Infrasound Signals Classification |
| title_fullStr | A Deep Learning Approach for Distant Infrasound Signals Classification |
| title_full_unstemmed | A Deep Learning Approach for Distant Infrasound Signals Classification |
| title_short | A Deep Learning Approach for Distant Infrasound Signals Classification |
| title_sort | deep learning approach for distant infrasound signals classification |
| topic | infrasound signal classification deep learning noise reduction station signal combination CNN |
| url | https://www.mdpi.com/1424-8220/25/7/2058 |
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