Quantitative Measures for Passive Sonar Texture Analysis
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912856333615104 |
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| author | Ritu, Jarin Van Dine, Alexandra Peeples, Joshua |
| author_facet | Ritu, Jarin Van Dine, Alexandra Peeples, Joshua |
| contents | Passive sonar signals contain complex characteristics often arising from environmental noise, vessel machinery, and propagation effects. While convolutional neural networks (CNNs) perform well on passive sonar classification tasks, they can struggle with statistical variations that occur in the data. To investigate this limitation, synthetic underwater acoustic datasets are generated that centered on amplitude and period variations. Two metrics are proposed to quantify and validate these characteristics in the context of statistical and structural texture for passive sonar. These measures are applied to real-world passive sonar datasets to assess texture information in the signals and correlate the performances of the models. Results show that CNNs underperform on statistically textured signals, but incorporating explicit statistical texture modeling yields consistent improvements. These findings highlight the importance of quantifying texture information for passive sonar classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantitative Measures for Passive Sonar Texture Analysis Ritu, Jarin Van Dine, Alexandra Peeples, Joshua Audio and Speech Processing Passive sonar signals contain complex characteristics often arising from environmental noise, vessel machinery, and propagation effects. While convolutional neural networks (CNNs) perform well on passive sonar classification tasks, they can struggle with statistical variations that occur in the data. To investigate this limitation, synthetic underwater acoustic datasets are generated that centered on amplitude and period variations. Two metrics are proposed to quantify and validate these characteristics in the context of statistical and structural texture for passive sonar. These measures are applied to real-world passive sonar datasets to assess texture information in the signals and correlate the performances of the models. Results show that CNNs underperform on statistically textured signals, but incorporating explicit statistical texture modeling yields consistent improvements. These findings highlight the importance of quantifying texture information for passive sonar classification. |
| title | Quantitative Measures for Passive Sonar Texture Analysis |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.14843 |