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Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data
Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic si...
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| Pubblicato in: | Entropy (Basel) |
|---|---|
| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7512589/ https://ncbi.nlm.nih.gov/pubmed/33266713 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e20120990 |
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