TY  - JOUR
T1  - A New Depth Classification Method based on Underwater Acoustics for Naval Defense Applications
TT  - Deniz Savunma Uygulamaları için Sualtı Akustiğine Dayalı Yeni Bir Derinlik Sınıflandırma Yöntemi
AU  - Aydemir, Emrah
AU  - Yaman, Orhan
PY  - 2021
DA  - December
DO  - 10.31590/ejosat.1001051
JF  - Avrupa Bilim ve Teknoloji Dergisi
JO  - EJOSAT
PB  - Osman SAĞDIÇ
WT  - DergiPark
SN  - 2148-2683
SP  - 1
EP  - 7
IS  - 31
LA  - en
AB  - 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.
KW  - Underwater sound classification
KW  - S-transform
KW  - Deep Learning
KW  - AlexNet
KW  - SVM
N2  - 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.
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UR  - https://doi.org/10.31590/ejosat.1001051
L1  - https://dergipark.org.tr/en/download/article-file/1995785
ER  -