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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Detalles Bibliográficos
Autores principales: Shen, Sheng, Yang, Honghui, Li, Junhao, Xu, Guanghui, Sheng, Meiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512589/
https://www.ncbi.nlm.nih.gov/pubmed/33266713
http://dx.doi.org/10.3390/e20120990

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512589/
https://www.ncbi.nlm.nih.gov/pubmed/33266713
http://dx.doi.org/10.3390/e20120990

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