Marine ambient noise (AN) is a nonlinear and unstable signal, traditional dispersion entropy can only analyze the marine AN from a single scale, which is easy to cause the loss of information. To address this problem, we introduced multiscale dispersion entropy (MDE), and then a new feature extraction method of marine ambient noise based on MDE is proposed. We used MDE, multiscale permutation entropy (MPE), multiscale permutation Lempel–Ziv complexity (MPLZC), and multi-scale dispersion Lempel–Ziv complexity (MDLZC) to carry out feature extraction and classification recognition experiments for six ANs. The experimental results show that for the feature extraction methods based on MDE, MPE, MDLZC, and MPLZC, with the increase of the number of features, the feature extraction effect becomes better, and the average recognition rate (ARR) becomes higher; compared with other three feature extraction methods, the feature extraction method based on MDE has the best feature extraction effect and the highest ARR for the six ANs under the same feature number.
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