[1O1-GS-7] Vision, speech media processing: GAN

[1O1-GS-7-05] Generation of Impact-Echo Using GAN and Improvement of Accuracy on Impact-Echo Monitoring

〇Masafumi Okamoto¹, Hiroshi Shimbo², Sano Shunsuke², Toshiaki Mizobuchi², Tomoko Ozeki¹ (1. Tokai University, 2. Hosei University)

[[Online]]

Keywords:GAN, Impact-Echo Monitoring, Scalogram

In recent years, there has been a demand for comprehensive deterioration evaluation of aging concrete structures. One of the methods of comprehensive deterioration evaluation is impact-echo monitoring. In this paper, as a part of the quantification of impact-echo monitoring by machine learning model, we investigate the improvement of discrimination accuracy of the model by data augmentation of impact-echo sound. Based on the existing impact-echo sound data collected from real concrete structures, fake sound data is generated by a generative model using the GAN method to extend the training data for the discriminative model. By comparing the discriminative model trained on the training data consisting only of the existing impact-echo sound data and the discriminative model trained on the training data after the data augmentation, the improvement of the discrimination accuracy for the test data of impact-echo sound is demonstrated.