Mohammad Reza Khalilabadi - Faculty of Naval Aviation, Malek Ashtar University of Technology

The abstract should include the One of the most exciting topics for researchers over the past few years is detecting underwater acoustic noises. Meanwhile, the complicated nature of the ocean makes this task very challenging. Also, making signals formatted data compatible with machine learning approaches needs much knowledge in signal processing for feature detection. This paper proposed a method to overcome these challenges, which extracts features with Convolutional Neural Network (CNN) and Mel-spectrogram (converting signal data to images). This method needless knowledge in signal processing and more knowledge in machine learning; because using CNNs find the hidden pattern and knowledge of the data automatically. The proposed approach detected the presence of the ships and categorized them into different kinds of them with ۹۹% accuracy that is a noticeable improvement considering state of the art. The performed CNN models consist of ۲ CNN layers for feature extraction and a Dense layer for classification the underwater ship noises.

Underwater Acoustic, Deep Learning, recognition, Noise, CNN