@inproceedings{5f54d2deace548779906af6c66df841d,
title = "Combination of gated recurrent unit and network in network for underwater acoustic target recognition",
abstract = "Underwater acoustic target recognition is an important technical support for underwater acoustic information acquisition and confrontation. Taking the gated recurrent unit (GRU) into account which has an internal feedback mechanism reflecting the temporal correlation of underwater acoustic target features, a model combined with GRU and network in network (NIN) is proposed to recognize underwater acoustic targets. The proposed model integrates multi-channel information while compressing the hidden states of GRU, which enhances nonlinear fitting ability and local modeling ability of the network. Experiments based on measured underwater acoustic target signals show that the proposed model can achieve better classification performance than the multilayer stacked GRU model.",
author = "Shuang Yang and Xiangyang Zeng",
note = "Publisher Copyright: {\textcopyright} INTER-NOISE 2021 .All right reserved.; 50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021 ; Conference date: 01-08-2021 Through 05-08-2021",
year = "2021",
doi = "10.3397/IN-2021-1490",
language = "英语",
series = "Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering",
publisher = "The Institute of Noise Control Engineering of the USA, Inc.",
editor = "Tyler Dare and Stuart Bolton and Patricia Davies and Yutong Xue and Gordon Ebbitt",
booktitle = "Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering",
}