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New and Used Bills Classification Using Neural Networks
Dongshik KANG Sigeru OMATU Michifumi YOSHIOKA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1999/08/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)
transaction machine, adaptive digital filter, neural network,
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Classification of the new and used bills using the spectral patterns of raw time-series acoustic data (observation data) poses some difficulty. This is the fact that the observation data include not only a bill sound, but also some motor sound and noise by a transaction machine. We have already reported the method using adaptive digital filters (ADFs) to eliminate the motor sound and noise. In this paper, we propose an advanced technique to eliminate it by the neural networks (NNs). Only a bill sound is extracted from observation data using prediction ability of the NNs. Classification processing of the new and used bills is performed by using the spectral data obtained from the result of the ADFs and the NNs. Effectiveness of the proposed method using the NNs is illustrated in comparison with former results using ADFs.