Fatigue Level Estimation of Bill by Using Supervised SOM Based on Feature-Selected Acoustic Energy Pattern

Fatigued bills have harmful influence on daily operation of automated teller machine (ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. In this paper, we propose a new method to estimate fatigue levels of bills from feature-selected acoustic energy pattern of banking machines by using the supervised SOM. The proposed method also selects feature components of an acoustic energy pattern based on correlation between acoustic energy features and fatigue level of bill to let the supervised SOM work effectively. The experimental results with real bill samples show the effectiveness of the proposed method. Furthermore, we show an advantage of the proposed method by comparing it with another estimation method.