Deep Learning Based Classification of Underwater Acoustic Signals

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The classification of underwater acoustic targets is a challenging task due to the intricacies involved in soundscape, huge amounts of background interference, and the varied features of underwater targets. Various approaches have been explored for classifying underwater targets based on acoustic signatures. The problem with these conventional methods lies in the extensive domain-specific knowledge they demand for effective feature engineering. Alternatively, the deep learning approach is of great appeal for assistance to manual sonar operators who mainly rely on their expertise for the classification of targets. This work leverages the approach of deep neural networks for underwater acoustic target categorization, aiming to analyze whether we can reduce the need for extensive domain knowledge required, by letting deep learning algorithms itself find the deep audio embeddings. This approach implements an audio classifier designed to classify acoustic targets with input as log Mel-spectrograms. To compare the performance of this classifier, we also implement a regular neural network for audio classification with MFCCs as input features. The overall results of the model are promising.