Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

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  • Teranishi Masaru
    Department of Information Systems and Management, Faculty of Applied Information Science, Hiroshima Institute of Technology
  • Omatu Sigeru
    Graduate School of Engineering, Osaka Prefecture University
  • Kosaka Toshihisa
    Technology Development Dept., Glory Ltd.

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  • 教師ありＳＯＭを用いた紙幣音響特徴に基づく疲弊度推定
  • キョウシ アリ SOM オ モチイタ シヘイ オンキョウ トクチョウ ニ モトズク ヒヘイド スイテイ

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Abstract

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 propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

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