Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms
Open accessAbstract
Ship type classification with radiated noise helps monitor the noise of shipping around the hydrophone deployment site. This paper introduces a convolutional neural network with several auditory-like mechanisms for ship type classification. The proposed model mainly includes a cochlea model and an auditory center model. In cochlea model, acoustic signal decomposition at basement membrane is implemented by time convolutional layer with auditory filters and dilated convolutions. The transformation of neural patterns at hair cells is modeled by a time frequency conversion layer to extract auditory features. In the auditory center model, auditory features are first selectively emphasized in a supervised manner. Then, spectro-temporal patterns are extracted by deep architecture with multistage auditory mechanisms. The whole model is optimized with an objective function of ship type classification to form the plasticity of the auditory system. The contributions compared with an auditory inspired convolutional neural network include the improvements in dilated convolutions, deep architecture and target layer. The proposed model can extract auditory features from a raw hydrophone signal and identify types of ships under different working conditions. The model achieved a classification accuracy of 87.2% on four ship types and ocean background noise.
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Shen et al. do not report the exact devices used, the precise period of data collection, or the labelled data used, making it impossible to reproduce the reported results even though the original raw data are freely available.
“However, Shen et al. [101] do not report the exact devices that were used, the precise period of data collection, or the labelled data used. Therefore, even though the original raw data are freely available, it is impossible to reproduce the results reported in that work.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [101]
Section 4.1 (Experimental Dataset; page unavailable) directly contradicts the claim that Shen et al. did not report the exact device: it identifies an Ocean Sonics icListen AF hydrophone. It gives a 29-month collection duration and ship-type categories, but not precise calendar dates or the labels for individual recordings. Our copy does not establish that the raw data are freely available or that reproduction is impossible. (LLM, conf=0.85)
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Experimental — accuracy not yet measuredThe auditory-inspired CNN had a high accuracy (87.2%), outperforming a standard CNN (83.2%), a stack of autoencoders (81.4%), wavelet transform with PCA (74.6%), and a support-vector machine (68.2%).
“The results showed that the proposed auditory-inspired CNN had a high accuracy (87.2%), outperforming a standard CNN (83.2%), a stack of autoencoders [113] (81.4%) and various traditional object recognition algorithms, such as a combination of wavelet transform with PCA [114] (74.6%) and a support-vector machine [115] (68.2%).”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [101]
Section 4.2 (page unavailable) says the proposed model outperformed manually designed features and improved on a previous auditory-inspired CNN. Our copy does not substantiate the five quoted accuracy figures or the claimed ranking against a standard CNN, stacked autoencoders, wavelet transform with PCA, and an SVM. (LLM, conf=0.85)
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- Authors
- Shen, S., Yang, H., Yao, X., Li, J., Xu, G., & Sheng, M.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Yang, Honghui
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-01
- Volume
- 20
- Issue
- 1
- Pages
- 253
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6983013
- OpenAlex
- W2997819422
- Cited by
- 57
- References
- 26
- Citations by year
- 2026: 9 · 2025: 5 · 2024: 10 · 2023: 6 · 2022: 11 · 2021: 7 · 2020: 9
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Keywords
- ship classification, radiated noise, auditory filters, dilated convolution, time-frequency features, underwater acoustic signal processing
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