| dc.description.abstract | Before buying a house, an inspection is carried out to check whether the indoor floor tiles have a hollow sound when knocked and whether they meet the standards. The traditional home inspection method uses a medical stick to hit five points on the floor tiles (upper left, lower left, upper right, lower right, and center). Floor tile inspection relies too much on the judgment of experienced technicians, and the inspection standard is too subjective and cannot be recorded. Therefore, this paper takes deep learning in the field of machine learning as the core of solving the problem and embeds the hollow recognition function of floor tiles on the Arduino Nano 33 BLE Sense microcontroller so that it is easy to carry so that people or technicians can use it more simply and conveniently. The efficient way to test the floor tiles enables the public to test by themselves and record them numerically so that the test results are more credible. The hollow problem of floor tiles can be found early and can be reinforced or replaced immediately. This thesis first designs and uses 3D printing technology to make a hollow brick model, taps the floor brick with a medical stick and collects audio data with the built-in microphone of Arduino Nano 33 BLE Sense, divides all audio data into several categories, and conducts audio through Edge Impulse Preprocessing and extracting features using three methods: Spectrogram, Mel-filter bank energy (MFE) and MelFrequency Cepstral Coefficients (MFCC), using convolution Neural network (Convolutional Neural Networks, CNN) training model, then through verification and testing, compare the measurement indicators of the three methods of Spectrogram, MFE, and MFCC, and finally found that the Spectrogram model and the identification six thresholds of 0.75 have the highest accuracy, and the accuracy of the validation set is The accuracy of the test set is 97.7%, the accuracy of the test set is 92.48%, and the accuracy of the actual hitting the floor tile is up to 81.25%. Therefore, this model is deployed on the Arduino Nano 33 BLE Sense and installed on the self-designed diagnosis stick for direct use. The general public or inspectors can use the intelligent diagnostic stick to detect floor tiles at home or during house inspections. Through a simple tap, they can instantly identify whether the floor tiles are hollow and whether they need to be replaced or reinforced to reduce excessive reliance on subjective judgments and problems that cannot be recorded. | en_US |