Heterogeneous Multi-Branch Feature Fusion Architecture for Underwater Acoustic Target Recognition
By: Yuexiong Yang, Zhenhao Chu and Zilong Peng
References
- Thorp W H. Analytic description of the low‐frequency attenuation coefficient. Journal of the Acoustical Society of America 1967. https://doi.org/10.1121/1.1910566.
- Luo X, Chen L, Zhou H, Cao H. A survey of underwater acoustic target recognition methods based on machine learning. Journal of Marine Science and Engineering 2023. https://doi.org/10.3390/jmse11020384.
- Sun Q, Zhou H. An acoustic sea glider for deep-sea noise profiling using an acoustic vector sensor. Polish Maritime Research 2022. https://doi.org/https://doi.org/10.2478/pomr-2022-0006.
- Zieja M, Wawrzyński W, Tomaszewska J, Sigiel N. A method for the interpretation of sonar data recorded during autonomous underwater vehicle missions. Polish Maritime Research 2022. https://doi.org/https://doi.org/10.2478/pomr-2022-0038.
- Zhang C, Xu Q, Yang H, Peng Z, Li J, Zhou J. Experimental study and numerical simulation of radiated noise from unmanned underwater vehicle. Polish Maritime Research 2024. https://doi.org/https://doi.org/10.2478/pomr-2024-0057.
- Chen Y, Xu X. The research of underwater target recognition method based on deep learning. Proc. 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017. https://doi.org/10.1109/ICSPCC.2017.8242464.
- Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 2002. https://doi.org/10.1109/5.18626.
- Wang Q, Zeng X, Wang L, Wang H, Cai H. Passive moving target classification via spectra multiplication method. IEEE Signal Processing Letters 2017. https://doi.org/10.1109/LSP.2017.2672601.
- Gent C, Sheppard C. Special feature: Predicting time series by a fully connected neural network trained by back propagation. Computing and Control Engineering 1992. https://doi.org/10.1049/cce:19920031.
- Hinton G E, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Computation 2006. https://doi.org/10.1162/neco.2006.18.7.1527.
- Lian Z, Xu K, Wan J, Li G. Underwater acoustic target classification based on modified GFCC features. Proc. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017. https://doi.org/10.1109/IAEAC.2017.8054017.
- Wang X, Zhang Y, Xiao Z, Huang M. IS3L: An integrated self-training semi-supervised learning strategy for underwater acoustic target detection. Applied Acoustics 2023. https://doi.org/10.1016/j.apacoust.2023.109477.
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015. https://doi.org/10.1038/nature14539.
- Piczak K J. Environmental sound classification with convolutional neural networks. Proc. 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015. https://doi.org/10.1109/MLSP.2015.7324337.
- Honghui Y, Junhao L, Meiping S. Underwater acoustic target multi-attribute correlation perception method based on deep learning. Applied Acoustics 2022. https://doi.org/10.1016/j.apacoust.2022.108644.
- Shen S, Yang H, Li J, Xu G, Sheng M. Auditory inspired convolutional neural networks for ship type classification with raw hydrophone data. Entropy 2018. https://doi.org/10.3390/e20120990.
- Han Y, Kim J, Lee K. Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Transactions on Audio, Speech, and Language Processing 2016. https://doi.org/10.1109/TASLP.2016.2632307.
- Zhang Q, Da L, Zhang Y, Hu Y. Integrated neural networks based on feature fusion for underwater target recognition. Applied Acoustics 2021. https://doi.org/10.1016/j.apacoust.2021.108261.
- Pan X, Sun J, Feng T, Lei M, Wang H, Zhang W. Underwater target recognition based on adaptive multi-feature fusion network. Multimedia Tools and Applications 2025. https://doi.org/10.1007/s11042-024-19178-9.
- Seo S, Kim C, Kim J-H. Convolutional neural networks using log mel-spectrogram separation for audio event classification with unknown devices. Journal of Web Engineering 2022. https://doi.org/https://doi.org/10.13052/jwe1540-9589.21216.
- Khalilabadi M R. Underwater ship-radiated acoustic noise recognition based on mel-spectrogram and convolutional neural network. International Journal of Coastal, Offshore and Environmental Engineering (IJCOE) 2023. https://doi.org/https://doi.org/10.22034/ijcoe.2023.166732.
- Sareen V, Seeja K. Speech emotion recognition using mel spectrogram and convolutional neural networks (CNN). Procedia Computer Science 2025. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.624.
- Huzaifah M. Comparison of time-frequency representations for environmental sound classification using convolutional neural networks. arXiv preprint arXiv:1706.07156 2017. https://doi.org/10.48550/arXiv.1706.07156.
- Bi F, Yang L. Research on acoustic scene classification based on time–frequency–wavelet fusion network. Sensors 2025. https://doi.org/10.3390/s25133930.
- Meng X et al. A multi-time-frequency feature fusion approach for marine mammal sound recognition. Journal of Marine Science and Engineering 2025. https://doi.org/10.3390/jmse13061101.
- Pham L, Phan H, Nguyen T, Palaniappan R, Mertins A, McLoughlin I. Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework. Digital Signal Processing 2021. https://doi.org/10.1016/j.dsp.2020.102943.
- Zheng W, Mo Z, Xing X, Zhao G. CNNs-based acoustic scene classification using multi-spectrogram fusion and label expansions. arXiv preprint arXiv:1809.01543 2018. https://doi.org/10.48550/arXiv.1809.01543.
- Irfan M, Jiangbin Z, Ali S, Iqbal M, Masood Z, Hamid U. DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification. Expert Systems with Applications 2021. https://doi.org/10.1016/j.eswa.2021.115270.
- Zhu P, Zhang Y, Huang Y, Zhao C, Zhao K, Zhou F. Underwater acoustic target recognition based on spectrum component analysis of ship radiated noise. Applied Acoustics 2023. https://doi.org/10.1016/j.apacoust.2023.109552.
Language: English
Page range: 145 - 155
Published on: May 6, 2026
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2026 Yuexiong Yang, Zhenhao Chu, Zilong Peng, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution 4.0 License.