Separation of underwater acoustic signals based on C-RNN network

ZongBin Shi; KeJun Wang

doi:10.1117/12.2635805

6 May 2022 Separation of underwater acoustic signals based on C-RNN network

ZongBin Shi, KeJun Wang

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 1225607 (2022) https://doi.org/10.1117/12.2635805
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

The sound signal can be transmitted over a long distance in the water environment, but there is often interference from other signal sources in the real environment, which will seriously reduce the sensitivity and recognizability of the underwater acoustic signal. At this time, it is necessary to use underwater acoustic signal separation technology to separate mixed underwater acoustic signals. Due to the time sequence of the audio signal, the feature extraction ability of the separation model for the input sound signal largely determines the performance of the model. We propose a C-RNN network model that combines convolutional and recurrent neural network to achieve the improvement of separation performance. The advantages and disadvantages of separation based on time domain and frequency domain are compared, and a hybrid coding module is proposed to achieve a new state-of-the-art for underwater acoustic signal separation.

Citation Download Citation

ZongBin Shi and KeJun Wang "Separation of underwater acoustic signals based on C-RNN network", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 1225607 (6 May 2022); https://doi.org/10.1117/12.2635805

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