Underwater Acoustic Signal Classification Based on Sparse Time–Frequency Representation and Deep Learning
Open accessAbstract
For classification of underwater acoustic signals, we propose a novel sparse anisotropic chirplet transform (ACT) to reveal fine time-frequency structures. The signal features in the form of a time-frequency map are fed into a deep convolutional neural network, referred to as a time-frequency feature network (TFFNet), which brings flexibility to signal classification. The TFFNet is based on a novel efficient feature pyramid enhancing feature (EFP) maps by aggregating the context information at different scales. To remove the gridding artifacts on enhanced feature maps, a form of aggregating transformation, a forward feature fusion, is utilized to merge the forward feature maps. Main contributions of this work are a novel sparse ACT, a TFFNet classifier, and an EFP with forward feature fusion. Experimental results demonstrate that the sparse ACT provides a high-resolution time-frequency representation of underwater signals and the TFFNet improves the classification performance compared to known networks and two machine learning methods (random forest and support vector machine with radial basis function kernel) on two real data sets, an underwater acoustic communication signal data set and whale sounds data set.
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This result was attributed to ACT generating a high-resolution time-frequency representation, providing features that would not be achieved using other known transforms such as STFT.
“This result was attributed to the fact that ACT generated a high-resolution time–frequency representation, providing features that would not be achieved using other known transforms, such as STFT.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [117]
The combination of ACT with the EFP method achieved higher accuracy with less resources (memory and time) compared to Random Forest and Support Vector Machines.
“The combination of ACT with the EFP method was compared against Random Forest and Support Vector Machines, achieving higher accuracy with less resources (memory and time).”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [117]
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
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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
“ACT was used in [117] as a preprocessing stage for an underwater acoustic communication dataset and for identifying whale sounds, then it was fed into a deep CNN, called time—frequency feature network (TFFNet).”
Method
- Authors
- Miao, Y., Zakharov, Y. V., Sun, H., Li, J., & Wang, J.
- Affiliations
- Xiamen University (CN)
- University of York (GB)
- University of Southampton (GB)
- Tianjin University of Technology (CN)
- Venue
- IEEE Journal of Oceanic Engineering
- Published
- 2021-07
- Volume
- 46
- Issue
- 3
- Pages
- 952-962
- ISSN
- 0364-9059 (ISSN-L)
- 1558-1691
- 2373-7786
- OpenAlex
- W3119019295
- Cited by
- 77
- References
- 1
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 21 · 2023: 13 · 2022: 8
- FWCI
- 5.05
- Type
- journal article
- Language
- en
- Open access
- green
- orcid.org
- Licence
- Locations
- IEEE Journal of Oceanic Engineering (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Funders
- National Key R and D Program of China (2018YFC0809200)
- National Natural Science Foundation of China (61671394 61971362)
- European Unions Horizon 2020 Research and Innovation Programme (654462)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- time-frequency representation, convolutional neural networks, feature pyramid, whale sounds
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