A method for underwater acoustic signal classification using convolutional neural network combined with discrete wavelet transform

The detection and classification of underwater targets such as fish are one of the major tasks of the underwater acoustic signal processing and are very important for scientific, fisheries and ocean engineering and economic fields. The convolutional neural network (CNN) combined with the discrete wavelet transform (DWT) (namely CNN_DWT) not only reduces the data processing dimension of signals and the computational costs of the signal analysis, but also improves the performance of target detection and classification. This paper proposes a new CNN to classify the images that reflected the underwater acoustic signal in the database that is made up of the scalogram of underwater acoustic signals. Also, in order to attain greater accuracy and comparable efficiency to the spatial domain processing, we convert the data to the wavelet domain. Also, we propose a deep learning method for the classification of underwater acoustic signals using the new CNN combined with DWT. Next, through the simulation experiment, we evaluate our new method for underwater acoustic signal classification using the CNN combined with DWT, by comparing with classical methods. Comparing the proposed method to spatial domain CNN and classical methods, the experimental results reveal a substantial increment in classification accuracy and noise robustness. And the learning curves show that the proposed CNN_DWT does not generate the overfitting problem and its generalization ability is high. The proposed CNN_DWT improves the classification accuracy and convergence of underwater acoustic signals than the classical CNNs. The noise robustness of the proposed CNN_DWT is higher than those of classical CNNs and back-propagation neural networks (BPNNs) for the classification of underwater acoustic signals. Experimental results show that the classification performance of new CNN combined with DWT is higher than those of classical CNNs and BPNNs for the classification of underwater acoustic signals.