1,417
Views
33
CrossRef citations to date
0
Altmetric
Original Article

Deep learning-based framework for expansion, recognition and classification of underwater acoustic signal

, , &
Pages 205-218 | Received 26 Oct 2018, Accepted 11 Jul 2019, Published online: 05 Aug 2019

References

  • Abouelnaga, Y., Ali, O. S., Rady, H., & Moustafa, M. (2016). Cifar-10: Knn-based ensemble of classifiers. 2016 International Conference on Computational Science and Computational Intelligence. CSCI,(pp.1192–1195). Las Vegas, NV, USA. IEEE.
  • Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein gan.International Conference on Machine Learning. Sydney, Australia.
  • Berthelot, D., Schumm, T., & Metz, L. (2017). Began: Boundary equilibrium generative adversarial networks.
  • Chazal, P. D., Tapson, J., & Schaik, A. V. (2015). A comparison of extreme learning machines and back-propagation trained feed-forward networks processing the mnist database. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, (pp.2165–2168). Brisbane, QLD, Australia. IEEE.
  • Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets.
  • Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W. F., & Sun, J. (2017). Generating multi-label discrete electronic health records using generative adversarial networks.
  • Durak, L., & Arikan, O. (2003). Short-time fourier transform: Two fundamental properties and an optimal implementation. IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society, 51(5), 1231–1242.
  • El-Darymli, K., Gill, E. W., Mcguire, P., Power, D., & Moloney, C. (2017). Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review. IEEE Access, (Vol.4, pp.6014–6058).IEEE.
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2014). Generative adversarial nets. International Conference on Neural Information Processing Systems, (Vol.3, pp.2672–2680). Montreal, Canada MIT Press.
  • Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of wasserstein gans.
  • Haarlem, M. P. V. (2016). Lofar: The low frequency array. Astronomy & Astrophysics, 556(7), 629–635.
  • Han, S., Mao, H., & Dally, W. J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. Fiber, 56(4), 3–7.
  • Hu, G., Wang, K., Peng, Y., Qiu, M., Shi, J., & Liu, L. (2018). Deep learning methods for underwater target feature extraction and recognition. Computational Intelligence and Neuroscience, 2018,(2018-3-27), 2018(3), 1–10.
  • Jager, J., Wolff, V., Fricke-Neuderth, K., Mothes, O., & Denzler, J. (2017). Visual fish tracking: Combining a two-stage graph approach with CNN-features. Oceans, (pp.1–6). Aberdeen, UK. IEEE.
  • Kaiyrbekov, N., Krestinskaya, O., & James, A. P. (2018). Variability analysis of memristor-based sigmoid function. 2018 International Conference on Computing and Network Communications. Astana, Kazakhstan.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, (Vol.60, pp.1097–1105). Nevada, USA, Curran Associates Inc.
  • Lecun, Y. (1998). Lenet-5, convolutional neural networks. Retrieved from: yann.lecun.com.
  • Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
  • Li, X., Shang, M., Hao, J., & Yang, Z. (2016). Accelerating fish detection and recognition by sharing CNNs with objectness learning. Shanghai, China. Oceans, (pp.1–5). IEEE.
  • Li, Y., & Zhe, C. (2017).Entropy based underwater acoustic signal detection. International Bhurban Conference on Applied Sciences & Technology. Islamabad, Pakistan.IEEE.
  • Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. Computer Science, (Vol.6, pp.2672–2680).
  • Mohamed, A. R., Hinton, G., & Penn, G. (2012). Understanding how deep belief networks perform acoustic modelling. IEEE International Conference on Acoustics, Speech and Signal Processing. Kyoto, Japan. IEEE,(Vol.1, pp.4273–4276). IEEE.
  • Nishida, S., Iwase, R., Kawaguchi, K., Matsuo, I., & Akamatsu, T. (2017). Real time detection and localization system for underwater acoustic signal with cable observatories in the west Pacific Ocean. Techno-ocean,(pp.544–547). Kobe, Japan. IEEE
  • Odena, A., Olah, C., & Shlens, J. (2016). Conditional image synthesis with auxiliary classifier gans.
  • Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. International Conference on Learning Representations. San Juan, Puerto Rico.
  • Uchida, K., Tanaka, M., & Okutomi, M. (2017). Coupled convolution layer for convolutional neural network. Cancun, Mexico. International Conference on Pattern Recognition, (Vol.105, pp.197). IEEE.
  • Valdenegro-Toro, M. (2016). Object recognition in forward-looking sonar images with convolutional neural networks. Oceans, (pp.1–6). Monterey, CA, USA. IEEE.
  • Yang, J., Kannan, A., Batra, D., & Parikh, D. (2017). LR-GAN: Layered recursive generative adversarial networks for image generation.
  • Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003). Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. IEEE International Conference on Data Mining, (pp.427–434). Melbourne, FL, USA. IEEE.
  • Yuan, B. (2017). Efficient hardware architecture of softmax layer in deep neural network. System-On-Chip Conference, (pp.323–326). Seattle, WA, USA. IEEE.
  • Yue, H., Zhang, L., Wang, D., Wang, Y., & Lu, Z. (2017). The classification of underwater acoustic targets based on deep learning methods. International Conference on Control, Automation and Artificial Intelligence. Wuhan, China.
  • Zhang, C., & Woodland, P. C. (2016). DNN speaker adaptation using parameterised sigmoid and ReLU hidden activation functions. IEEE International Conference on Acoustics, Speech and Signal Processing, (pp.5300–5304). Shanghai, China. IEEE.
  • Zhu, P., Isaacs, J., Fu, B., & Ferrari, S. (2018). Deep learning feature extraction for target recognition and classification in underwater sonar images, Conference on Decision and Control, (pp.2724–2731). Melbourne, VIC, Australia. IEEE.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.