Abstract:In recent years, deep neural networks have been successfully applied to solve a range of detection and classification tasks in underwater acoustics, outperforming existing methods. However, deep learning models are “data hungry” requiring large amounts of accurately labelled acoustic samples to train. Moreover, a certain amount of “fine tuning” is often required to achieve satisfactory performance in a new acoustic environment. Thus, the development of deep learning models depends on the input of expert human … Show more
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