Deniz Savunma Uygulamaları için Sualtı Akustiğine Dayalı Yeni Bir Derinlik Sınıflandırma Yöntemi

Bu araştırmanın temel amacı, yüksek performanslı otomatik bir sualtı akustik sınıflandırma modeli sunmaktır. Böylece yeni bir ses veri seti toplanmıştır. Bu veri seti kullanılarak, bu çalışmada yeni bir sualtı derinlik sınıflandırma yöntemi önerilmiştir. Sualtı seslerinin ön işlemesi için ortalama havuzlama kullanılmıştır. Kullanılan ortalama havuzlama modeli hem gürültüleri hem de sıkıştırılmış sinyali ortadan kaldırmıştır. Özellik çıkarımı için S-dönüşüm ve AlexNet kullanılmıştır. S-dönüşümünün su altı seslerine yerleştirilmesiyle kontur görüntüleri elde edilmiştir. Bu görüntüler AlexNet'in girdisi olarak kullanılmıştır. Burada, transfer öğrenme kullanılarak öznitelikleri çıkarmak için AlexNet kullanılmıştır. Çıkarılan özellikler Destek Vektör Makinesi (SVM) ile sınıflandırılmıştır. Bizim yöntemimizde %99,05 doğruluk hesaplanmıştır. Hesaplanan sonuçlar ve bulgular, su altı ses sınıflandırmasında önerilen S-dönüşüm ve AlexNet tabanlı modelimizin başarısını açıkça göstermektedir.

Anahtar Kelimeler:

Sualtı ses sınıflandırması, S-dönüşümü, Derin Öğrenme, AlexNet, SVM

A New Depth Classification Method based on Underwater Acoustics for Naval Defense Applications

The main purpose of this research is to present an automatic underwater acoustic classification model with high performance. Thus, a new sound dataset was collected. By using this dataset, a new underwater depth classification method is proposed in this work. Average pooling has been used to pre-processing underwater sounds. The used average pooling model is both removed the noises and compressed signal. S-transform and AlexNet have been used for feature extraction. By deploying S-transform to underwater sounds, contour images have been obtained. These images have been utilized input of the AlexNet. Herein, AlexNet has been utilized to extract features by using transfer learning. Features extracted have been classified with the Support Vector Machine (SVM). In our method, 99.05% accuracy has been calculated. The calculated results and findings obviously illustrate the success of our proposed S-transform and AlexNet based model on the underwater sound classification.

Keywords:

Underwater sound classification, S-transform, Deep Learning, AlexNet, SVM,

PDF

___

Aydemir, E., Tuncer, T., & Dogan, S. (2020). A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Medical Hypotheses, 134(December 2019), 109519. doi: 10.1016/j.mehy.2019.109519
Bedi, P., Mewada, S., Vatti, R. A., Singh, C., Dhindsa, K. S., Ponnusamy, M., & Sikarwar, R. (2021). Detection of attacks in IoT sensors networks using machine learning algorithm. Microprocessors and Microsystems, 82(December 2020), 103814. doi: 10.1016/j.micpro.2020.103814
Das, M. K., & Ari, S. (2013). Analysis of ECG signal denoising method based on S-transform. Irbm, 34(6), 362–370. doi: 10.1016/j.irbm.2013.07.012
Fan, X., Wu, J., Shi, P., Zhang, X., & Xie, Y. (2018). A novel automatic dam crack detection algorithm based on local-global clustering. Multimedia Tools and Applications, 77(20), 26581–26599. doi: 10.1007/s11042-018-5880-1
Fischell, E. M., Viquez, O., & Schmidt, H. (2018). Passive acoustic tracking for behavior mode classification between surface and underwater vehicles. IEEE International Conference on Intelligent Robots and Systems, 2383–2388. doi: 10.1109/IROS.2018.8593981
Fırat, U., & Akgül, T. (2017). Gemi Akustik İz Analizi. EMO Bilimsel Dergi, 7(13), 25–31.
Gowtham, S., Keerthana, I., & Balaji, M. (2019). Characterization and Classification of Hall Sensor Faults using S-Transform Analysis on BLDC Motor Drive. 2019 IEEE 1st International Conference on Energy, Systems and Information Processing, ICESIP 2019. doi: 10.1109/ICESIP46348.2019.8938284
Haryanto, T., Sitanggang, I. S., Agmalaro, M. A., & Rulaningtyas, R. (2020). The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification. CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, 34–38. doi: 10.1109/CENIM51130.2020.9297895
Jiang, J., Wu, Z., Lu, J., Huang, M., & Xiao, Z. (2020). Interpretable features for underwater acoustic target recognition. Measurement: Journal of the International Measurement Confederation, 108586. doi: 10.1016/j.measurement.2020.108586
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097–1105. doi: 10.1201/9781420010749
Liu, Y., Chen, H., & Wang, B. (2021). DOA estimation based on CNN for underwater acoustic array. Applied Acoustics, 172, 107594. doi: 10.1016/j.apacoust.2020.107594
Neves, G., Ruiz, M., Fontinele, J., & Oliveira, L. (2020). Rotated object detection with forward-looking sonar in underwater applications. Expert Systems with Applications, 140, 112870. doi: 10.1016/j.eswa.2019.112870
Reis, C. D. G., Padovese, L. R., & de Oliveira, M. C. F. (2019). Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature. Methods in Ecology and Evolution, 10(9), 1501–1516. doi: 10.1111/2041-210X.13245
Santos-Domínguez, D., Torres-Guijarro, S., Cardenal-López, A., & Pena-Gimenez, A. (2016). ShipsEar: An underwater vessel noise database. Applied Acoustics, 113, 64–69. doi: 10.1016/j.apacoust.2016.06.008
Sierra, E., & Contreras, J. (2015). Classification of small boats using fuzzy classifier. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 2015-Septe, 0–4. doi: 10.1109/NAFIPS-WConSC.2015.7284174
Song, W., Wang, Y., Huang, D., & Tjondronegoro, D. (2018). A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11164 LNCS, 678–688. doi: 10.1007/978-3-030-00776-8_62
Stockwell, R. G. (1996). Localization of the complex spectrum: the s transform. IEEE Transactions on Signal Processing, 44(4), 993. doi: 10.1109/78.492555
Stockwell, R. G. (2007). A basis for efficient representation of the S-transform. Digital Signal Processing: A Review Journal, 17(1), 371–393. doi: 10.1016/j.dsp.2006.04.006
Yaman, O., Ertam, F., Tuncer, T., & Firat Kilincer, I. (2020). Automated UHF RFID‐based book positioning and monitoring method in smart libraries. IET Smart Cities, 2(4), 173–180. doi: 10.1049/iet-smc.2020.0033
Yaman, O., & Tuncer, T. (2021). Ensemble NASNet Deep Feature Generator Based Underwater Acoustic Classification Model. Veri Bilimi, 4(2), 33–39.