Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Tsubasa Fukumura, Hayato Aratame, Atsushi Ito, Masafumi Koike, Katsuhiko Hibino


The demand for inspections has increased due to the aging of concrete structures and tile wall surfaces. The hammering test is a simple inspection method, but the inspector needs much experience distinguishing the hammering sounds. Therefore, we developed a device that automatically classifies the hammering sounds using deep learning. However, the hardware with GPU for deep learning is the one-board microcomputer, which has no display, a battery, or an input device, so the inspector cannot change the settings or check the status during the hammering test. Therefore, we used a smartphone instead of a one-board microcomputer. We also used a cloud GPU since a smartphone does not have GPU. The results showed that communication time was the bottleneck. So we considered using a 5G network and compared the classification time, training time, and battery life of the smartphone. As a result, although the training time remained the same, we found that the classification speed was 1.46 times faster than the conventional method, and the smartphone’s battery life was sufficient. 


5G network; Neural Network; Hammering test; Non-destructive testing

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