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Proceedings Paper

Expansion of restricted sample for underwater acoustic signal based on generative adversarial networks
Author(s): Fan Liu; Qingzeng Song; Guanghao Jin
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Paper Abstract

Recently, deep learning has developed rapidly, which has made significant progress in tasks such as target detection and classification. Compared with traditional methods, using deep learning techniques contribute to achieve higher detection accuracy, recognition rate, and other better performance with big data set. In the fields of radar and sonar especially like underwater acoustic signals, training samples are scarce due to the difficulty of the collection or security reason, which leads to poor performance of the classification models, as those need big training samples. In this paper, we present a novel framework based on Generative Adversarial Networks (GAN) to resolve the problem of insufficient samples for the underwater acoustic signals. Our method preprocesses the audio samples to the gray-scale spectrum images, so that, those can fit the GAN to captures the features and reduce the complexity at the same time. Then our method utilizes an independent classification network outside the GAN to evaluate the generated samples by GAN. The experimental results show that the samples generated by our approach outperform existing methods with higher quality, which can significantly improve the prediction accuracy of the classification model.

Paper Details

Date Published: 6 May 2019
PDF: 8 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106948 (6 May 2019); doi: 10.1117/12.2524173
Show Author Affiliations
Fan Liu, Tianjin Polytechnic Univ. (China)
Qingzeng Song, Tianjin Polytechnic Univ. (China)
Guanghao Jin, Tianjin Polytechnic Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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