DES-Pat: A novel DES pattern-based propeller recognition method using underwater acoustical sounds. (April 2021)

• Record Type: Journal Article
• Authors: Yaman, Orhan; Tuncer, Turker; Tasar, Beyda
• Is Part Of: Applied acoustics. Volume 175(2021)
• Publication Date: 2021-04
• Subjects: Underwater acoustic -- Sound classification -- DES-Pat -- Propeller recognition; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2020.107859
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: New nonlinear feature generator is presented using first S-Box of the DES. Sounds of five types propellers were collected was published publicly. The recommended method attained 99.8% accuracy. Abstract: Purpose: This work aims to propose a new propeller recognition (propeller type classification) method by using a nonlinear pattern-based sound classification model with high prediction. This model consists of feature generation, feature selection, and classification phases. To test this model, five types of propellers are produced using a 3D printer. These propellers are categorized using number of wings. Material and Method: An experimental data collection environment was created and underwater sounds of these propellers were collected (available at (https://github.com/orhanyaman/Propeller )). To generate features from these sounds, a new nonlinear feature generation function is presented by using one of the substitution boxes (S-Box) of the data encryption standard (DES) block cipher. This S-Box determines the patterns. Therefore, this feature selector is called as DES-Pat. Results: The proposed DES-Pat generates a feature vector with a size of 512. By using Neighborhood Component Analysis (NCA), 150 the most valuable features were selected. The selected feature vector with a size of 150 was utilized as the input of the selected 12 shallow classifiers in 3 categories: Decision Tree, k Nearest Neighbor (KNN), and Support Vector Machine (SVM). Conclusion: The results show that these methods are very successful for underwater acoustical sound classification since Quadratic and Cubic SVMs reached 99.8% classification accuracies.