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

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

doi:10.15803/ijnc.12.2_359

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Our investigation substantially enhances existing methods in tile defect detection, as evidenced by a comprehensive comparison with prior studies [5][6][7][8][9][10][11][12][13][14] delineated in table 3. Our approach introduces a holistic improvement in several key performance indicators, warranting a detailed discussion on each advancement.…”

Section: Comparative Analysis In Tile Defect Detectionmentioning

confidence: 70%

Real-time hollow defect detection in tiles using on-device tiny machine learning

Lin,

Chang,

Zhuang

et al. 2024

Meas. Sci. Technol.

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This study addresses the challenge of subsurface defect detection in floor tiles for quality control in residential construction. To overcome the limitations of traditional inspection methods and the complexities associated with existing artificial intelligence (AI)-based approaches, we have developed the AI Diagnostic Stick (AID-Stick), a novel tool designed to advance the field of tile defect detection. This innovative tool integrates an embedded machine-learning framework, leveraging convolutional neural networks (CNNs) and tiny machine learning (TinyML) techniques. The AID-Stick utilizes spectrogram, Mel-frequency cepstral coefficient (MFCC), and Mel filterbank energy (MFE) for real-time, on-microcontroller unit (MCU) diagnostics of auditory signals from tile tapping tests. Our methodology effectively utilizes these acoustic features in distinguishing between intact and subsurface hollow defective tiles. The study's findings, revealing a notable validation accuracy of 97% and a real-world accuracy of 81.25%, showcase a promising improvement over traditional methods. The AID-Stick's practicality, cost-effectiveness, and user-friendly design make it potentially beneficial for small-to-medium enterprises and economically constrained markets. Furthermore, this research opens avenues for future enhancements in embedded AI systems, with potential applications extending beyond the construction industry to other domains requiring non-destructive testing. This work not only contributes to the field of industrial quality control but also to the development of intelligent diagnostic tools, paving the way for future innovations in automated inspection technologies.

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Section: Comparative Analysis In Tile Defect Detectionmentioning

confidence: 70%

Real-time hollow defect detection in tiles using on-device tiny machine learning

Lin,

Chang,

Zhuang

et al. 2024

Meas. Sci. Technol.

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“…So, we have used neural networks (NN) and Transfer Learning (TL) [9] for the hammering test to check the injured part by using the hitting sound. We targeted a tile wall and demonstrated our approach's effectiveness for tile walls commonly used in apartments [10].…”

Section: Hammering Test and Aimentioning

confidence: 99%

Smart non-destractive test of a concrete wall using a hammer

Ito,

Koike,

Hibino

2024

International Journal of Electronics and Telecommunications

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Large concrete structures such as buildings, bridges, and tunnels are aging. In Japan and many other countries, those built during economic reconstruction after World War II are about 60 to 70 years old, and flacking and other problems are becoming more noticeable. Periodic inspections were made mandatory by government and ministerial ordinance during the 2013-2014 fiscal year, and inspections based on the new standards have just begun. There are various methods to check the soundness of concrete, but the hammering test is widely used because it does not require special equipment. However, long experience is required to master the hammering test. Therefore, mechanization is desired. Although the difference between the sound of a defective part and a normal part is very small, we have shown that neural network is useful in our research. To use this technology in the actual field, it is necessary to meet the forms of concrete structures in various conditions. For example, flacking in concrete exists at various depths, and it is impossible to learn about flacking in all cases. This paper presents the results of a study of the possibility of finding flacking at different depths with a single inspection learning model and an idea to increase the accuracy of a learning model when we use a rolling hammer.

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Hammering Test on a Concrete Wall Using Neural Network

Ito

Yang

et al. 2023

Lecture Notes in Networks and Systems

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

Cited by 5 publications

References 10 publications

Real-time hollow defect detection in tiles using on-device tiny machine learning

Real-time hollow defect detection in tiles using on-device tiny machine learning

Smart non-destractive test of a concrete wall using a hammer

Hammering Test on a Concrete Wall Using Neural Network

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