DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification

作者：

Highlights：

• Proposed dataset consists of real underwater recordings of 47 h 4 min of 265 ships.

• Recordings are from throughout the year with different sea states and noise levels.

• Study of 6 T-F features by 8 machine learning and deep learning methods on dataset.

• Proposed a separable convolutional autoencoder for better classification accuracy.

摘要

•Proposed dataset consists of real underwater recordings of 47 h 4 min of 265 ships.•Recordings are from throughout the year with different sea states and noise levels.•Study of 6 T-F features by 8 machine learning and deep learning methods on dataset.•Proposed a separable convolutional autoencoder for better classification accuracy.

论文关键词：Underwater acoustics,Ship classification,Underwater dataset,Deep convolutional network

论文评审过程：Received 30 January 2021, Revised 15 April 2021, Accepted 21 May 2021, Available online 5 June 2021, Version of Record 11 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115270