Sonar Signal Classification using Neural Networks
One of the most important topics in the sonar sound data processing is proposing a powerful classifier to detect the sound source. In this paper we propose a classifier with proper accuracy. First, proper features should be extracted from sound data; Features could extract from time or frequency domains. Whenever fastness is important, time features are most effective. Otherwise, frequency domain features can be used. According to the importance of fastness in sonar sound source detection, in this paper, performance of features such as autocorrelation, partial autocorrelation and linear prediction code which are time domain features compare with each other. After we select proper feature we design a powerful classifier to classify sonar sound; to do this we implement probabilistic neural network and test it with these features; In order to have high accuracy for sonar sound detection.
Keywords: Neural Network, Partial Autocorrelation Coefficient, Autocorrelation Coefficient, Classifier
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ABOUT THE AUTHORS
Hossein Bahrami
H. Bahrami received B.Sc. degree in communication tells from ITI University in 2009. He is currently a M.Sc. student in Electronic Engineering at Islamic Azad University of Neyshabur. His research interest includes signal processing, neural network and image processing.
Seyyed Reza Talebiyan
R.Talebiyan received the PhD degree in Electronic Engineering from Ferdowsi University of Mashhad in 2009. He is currently assistant Professor of Imam-Reza International University. His research interests include Digital VLSI Circuits, Low power system and circuit design, Reconfigurable computing, Nano-CMOS circuit design, VLSI implementation of DSP systems and neural network.
Hossein Bahrami
H. Bahrami received B.Sc. degree in communication tells from ITI University in 2009. He is currently a M.Sc. student in Electronic Engineering at Islamic Azad University of Neyshabur. His research interest includes signal processing, neural network and image processing.
Seyyed Reza Talebiyan
R.Talebiyan received the PhD degree in Electronic Engineering from Ferdowsi University of Mashhad in 2009. He is currently assistant Professor of Imam-Reza International University. His research interests include Digital VLSI Circuits, Low power system and circuit design, Reconfigurable computing, Nano-CMOS circuit design, VLSI implementation of DSP systems and neural network.
