Fatigue level estimation of bill based on acoustic energy features by Supervised SOM

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. We proposed a new method to estimate fatigue levels of bills from acoustic energy feature of banking machines by using the supervised SOM in the previous study. Though the proposed method has achieved to estimate the bending rigidity from only the acoustic energy pattern effectively, there were some problems about estimation performances. In this paper, we try to improve estimation performance by reconsidering configurations of the Supervised SOM. Furthermore, we show effectiveness of the proposed method by comparing it with other estimation methods. The experimental results with real bill samples show the improved estimation accuracy, and effectiveness of the proposed method.