Classification of Fatigue Bill Based on Support Vector Machine by Using Acoustic Signal

Authors:
Dongshik Kang;Masaki Higa;Nobuo Shoji;Masanobu Fujita;Ikugo Mitsui
Affiliations:
, Okinawa, Japan 903-0213;, Okinawa, Japan 903-0213;, Osaka, Japan 547-0035;, Osaka, Japan 547-0035;, Osaka, Japan 547-0035
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

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Abstract

The bills in circulation generate a large amount of fatigue bills every year, causing various types of problems such as the paper jam in automatic tellers due to overworked and exhausted ones. An advanced technique is requested in order to classify the levels of fatigue as well as distinguish between the used and the new ones. Therefore, the purpose of this paper is to present the classification method of fatigue bills based on support vector machine(SVM) by using acoustic signals. The effectiveness of this approach is demonstrated by the bill identify experimentation based on the real acoustic signal.