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JACIII Vol.4 No.1 pp. 12-17
doi: 10.20965/jaciii.2000.p0012
(2000)

Paper:

Fast Neuro-Classification of New and Used Bills Using Spectral Patterns of Acoustic Data

Dongshik Kang, Sigeru Omatu and Michifumi Yoshioka

College of Engineering, Osaka Prefecture University 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan

Received:
October 5, 1998
Accepted:
February 20, 1999
Published:
January 20, 2000
Keywords:
Adaptive digital filter, LVQ algorithm, IA algorithm, Transaction machine
Abstract

An advanced neuro-classification of new and used bills using the spectral patterns is proposed. An acoustic spectral pattern is obtained from the output of the two-stage adaptive digital filters (ADFs) for time-series acoustic data. The acoustic spectral patterns are fed to a competitive neural network, and classified into some categories which show worn-out degrees of the bill. The proposed method is based on extension of an ADF, an individual adaptation (IA) algorithm, and a learning vector quantization (LVQ) algorithm. The experimental results show that the proposed method is useful to classify new and used bills.

Cite this article as:
Dongshik Kang, Sigeru Omatu, and Michifumi Yoshioka, “Fast Neuro-Classification of New and Used Bills Using Spectral Patterns of Acoustic Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.1, pp. 12-17, 2000.
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