Expansion of restricted sample for underwater acoustic signal based on generative adversarial networks

Fan Liu; Qingzeng Song; Guanghao Jin

doi:10.1117/12.2524173

6 May 2019 Expansion of restricted sample for underwater acoustic signal based on generative adversarial networks

Fan Liu, Qingzeng Song, Guanghao Jin

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 1106948 (2019) https://doi.org/10.1117/12.2524173
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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.

Citation Download Citation

Fan Liu, Qingzeng Song, and Guanghao Jin "Expansion of restricted sample for underwater acoustic signal based on generative adversarial networks", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106948 (6 May 2019); https://doi.org/10.1117/12.2524173

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available