GAO Deyang, GAO Dazhi*, LI Xiaolei
(College of Information Science and Engineering, Ocean University of China, Qingdao 266100, Shandong, China)
Abstract:
Deep neural networks were used to classify click signals of three typical marine mammals and impulse noise. First, the click signals of marine mammals were analyzed and a spectral energy algorithm was proposed. Then, the time domain signals were identified by the feedforward fully connected neural network and the influence of the parameters of the neural network on the recognition results was studied.Finally, the convolution neural network was used to identify the time-frequency signals. The recognition results show that the recognition accuracy of spectral energy algorithm is 69.83%. The accuracy of feedforward fully connected network can reach 98.28% by adjusting parameters, and the accuracy of convolutional neural network can reach 100%. Due to the small scale of experimental data and high signal-to-noise ratio of signals, the neural network has a good recognition effect.The research shows that the deep learning method can achieve better identification effect than the spectral energy algorithm, and the identification effect can be improved by adjusting the hidden layer parameters of the feedforward fully connected network.
KeyWords:
click signals; marine mammals; feedforward fully connected neural network; convolutional neural network