Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network
Open accessAbstract
This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods.
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The method in [120] requires the fundamental frequencies of the target object to be known beforehand, and assumes they are independent of environmental conditions.
“However, the fundamental frequencies of the target object need to be known beforehand, and it is assumed that they are independent of environmental conditions (which may not be true in the general case).”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [120]
Section 2.3 says the methods require a target frequency window to be defined and that unsuitable input parameters can make detection unfeasible. It does not say the target’s fundamental frequencies must be known beforehand. Nor does it assume independence from environmental conditions; Section 2.2 says underwater signals are influenced by many environmental factors. No page numbers are available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredResults described in [120] suggest that this method outperforms traditional classification methods.
“Results described in [120] suggest that this method outperforms traditional classification methods.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [120]
The performance of a standard CNN in classification of surface vehicle propeller cavitation noise in shallow water has been shown to be improved by analysing the amplitude variation of signal to detect the fundamental frequencies of VPCN.
“Analogously, the performance of a standard CNN in the classification of surface vehicle propeller cavitation noise (VPCN) in shallow water has shown to be improved by analysing the amplitude variation of signal to detect the fundamental frequencies of VPCN [120].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [120]
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Cited by
Sorted by citations
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“Analogously, the performance of a standard CNN in the classification of surface vehicle propeller cavitation noise (VPCN) in shallow water has shown to be improved by analysing the amplitude variation of signal to detect the fundamental frequencies of VPCN [120].”
BackgroundMethodInfluential
- Authors
- Bach, N. H., Vu, L. H., & Nguyen, V. D.
- Affiliations
- Institute of Electronics, Hoang Sam Street, Hanoi 10000, Vietnam
- School of Electronics and Telecommunications, Hanoi University of Science and Technology, Dai Co Viet Street, Hanoi 10000, Vietnam
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-05-12
- Volume
- 21
- Issue
- 10
- Pages
- 3353
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8151462
- OpenAlex
- W3162983744
- Cited by
- 24
- References
- 2
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 1 · 2023: 8 · 2022: 5
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- gold
- 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 › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Noise and Vibration Control
- Keywords
- propeller cavitation noise, spectrogram, amplitude variation, fundamental frequency extraction, convolutional neural networks, shallow water acoustics
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