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Babol Noshirvani University of Technology, Department of Electrical Engineering
Abstract
Identifying and classifying marine vehicles is of great significance in military applications. Traditionally, this task was performed by sonar officers based on acoustic signals received by sonar hydrophones. Nowadays, developing systems that perform this task automatically and with minimal human intervention is inevitable. In this study, using different augmentation methods, the size of this dataset is expanded under different scenarios. With the help of signal processing and machine learning knowledge, different features are extracted and next these features are classified using different classification algorithms. Finally, the performance of different scenarios is evaluated by calculating different measures. Results of this study show the success of the Mel-Frequency Cepsteral Coefficient (MFCC) feature extraction method when using various classification algorithms, including Artificial Neural Network (ANN) and also the failure of Convolutional Neural Network (CNN) when training with small datasets. The overall comparison reveals that due to the small number of samples in the dataset, ANN presents the best performance
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Afshar, S., Kazemitabar, J., & Ebrahimzadeh, A. (2023). An Effective Acoustic Signature Extraction Based on Optimization. Journal of Advanced Defense Science & Technology, 14(1), 23-38.
MLA
Soudabeh Afshar; Javad Kazemitabar; Ata Ebrahimzadeh. "An Effective Acoustic Signature Extraction Based on Optimization". Journal of Advanced Defense Science & Technology, 14, 1, 2023, 23-38.
HARVARD
Afshar, S., Kazemitabar, J., Ebrahimzadeh, A. (2023). 'An Effective Acoustic Signature Extraction Based on Optimization', Journal of Advanced Defense Science & Technology, 14(1), pp. 23-38.
VANCOUVER
Afshar, S., Kazemitabar, J., Ebrahimzadeh, A. An Effective Acoustic Signature Extraction Based on Optimization. Journal of Advanced Defense Science & Technology, 2023; 14(1): 23-38.
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