Fatigue Level Estimation of Bill based on Feature-Selected Frequency Band Acoustic Signal by using Supervised SOM

Fatigued bills have harmful influence on the daily operation of Automated Teller Machine(ATM). To make the classification of fatigued bills more efficient, the development of an automatic fatigued bill classification method is desirable. We propose a new method to estimate the fatigue level of bill from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves estimation accuracy of fatigue level of bills by adding frequency domain information to acoustic energy pattern. The experimental results with real bill samples shows the effectiveness of the proposed method. Key–Words: Neural networks, SOM, Fatigued bill classification, Signal processing, Industrial application