A method for underwater acoustic signal classification using convolutional neural network combined with discrete wavelet transform
Abstract
The detection and classification of underwater targets such as fish are one of the major tasks of the underwater acoustic signal processing and are very important for scientific, fisheries and ocean engineering and economic fields. The convolutional neural network (CNN) combined with the discrete wavelet transform (DWT) (namely CNN_DWT) not only reduces the data processing dimension of signals and the computational costs of the signal analysis, but also improves the performance of target detection and classification. This paper proposes a new CNN to classify the images that reflected the underwater acoustic signal in the database that is made up of the scalogram of underwater acoustic signals. Also, in order to attain greater accuracy and comparable efficiency to the spatial domain processing, we convert the data to the wavelet domain. Also, we propose a deep learning method for the classification of underwater acoustic signals using the new CNN combined with DWT. Next, through the simulation experiment, we evaluate our new method for underwater acoustic signal classification using the CNN combined with DWT, by comparing with classical methods. Comparing the proposed method to spatial domain CNN and classical methods, the experimental results reveal a substantial increment in classification accuracy and noise robustness. And the learning curves show that the proposed CNN_DWT does not generate the overfitting problem and its generalization ability is high. The proposed CNN_DWT…
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The use of wavelet transform was shown to be effective at enhancing noise robustness, achieving 99.7% classification accuracy with an SNR of 0 dB, greatly outperforming six other CNN architectures.
“The use of wavelet transform was shown to be effective at enhancing noise robustness in the system, achieving 99.7% classification accuracy with an SNR of 0 dB, greatly outperforming six other CNN architectures.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [118]
The reason behind the improvement in noisy environments was attributed to the application of a threshold and the DWT coefficients on the noise elimination stage, impacting directly the performance of the wavelet denoising.
“The reason behind the improvement on noisy environments was attributed to the application of a threshold and also to the DWT coefficients on the noise elimination stage, impacting directly the performance of the wavelet denoising.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [118]
Use of the augmented dataset in [118] reduced overfitting and improved generalisation ability.
“The results reported showed a reduction in overfitting and an improvement in generalisation ability with the use of the augmented dataset.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [118]
A data-augmentation procedure convolving an acoustic signal and an impulse response signal, then adding white Gaussian noise, generated a set of 30,000 images of 30 classes of underwater acoustic signals.
“Analogously, a data-augmentation procedure was performed in [118] by convolving an acoustic signal and an impulse response signal. Then, white Gaussian noise was added to finally produce the extended signals. After the data-augmentation process, a set containing 30,000 images of 30 different classes of underwater acoustic signals was generated.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [118]
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, a dataaugmentation procedure was performed in [118] by convolving an acoustic signal and an impulse response signal.”
BackgroundMethodInfluential
- Authors
- Kim, K.-I., Pak, M.-I., Chon, B.-P., & Ri, C.-H.
- Affiliations
- Kim Il-sung University (KP)
- ORCID
- Kim, Kyong-Il
- Venue
- International Journal of Wavelets, Multiresolution and Information Processing
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2021-01-25
- Volume
- 19
- Issue
- 04
- Pages
- 2050092
- ISSN
- 0219-6913 (ISSN-L)
- 1793-690X
- OpenAlex
- W3118027028
- Cited by
- 32
- References
- 0
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 10 · 2023: 4 · 2022: 4 · 2021: 1
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Wavelets Multiresolution and Information Processing (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Keywords
- convolutional neural networks, discrete wavelet transform, scalogram, fish detection, underwater object detection, noise robustness, overfitting
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