人の聴覚特性を考慮した機械学習によるタイル接着状況診断手法について

―ノイズ除去による効果―

抄録

In this study, we considered a method to perform automatic hammering inspection using machine learning based on auditory perception characteristics for diagnosis of tile adhesion condition in drone noise. In the hammering test at quiet condition, the condition could be evaluated accurately by machine learning. However, the accuracy was not enough at the noise condition. We then applied a noise reduction method using several microphones in addition to employing auditory perception characteristic of human in the estimation model. As the result, the accuracy could be improved significantly, and it was more accurate than the correct response rate by the subjective diagnosis.