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.
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Dongshik KANG, Sigeru OMATU, Michifumi YOSHIOKA, "New and Used Bills Classification Using Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 8, pp. 1511-1516, August 1999, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_8_1511/_p
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@ARTICLE{e82-a_8_1511,
author={Dongshik KANG, Sigeru OMATU, Michifumi YOSHIOKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={New and Used Bills Classification Using Neural Networks},
year={1999},
volume={E82-A},
number={8},
pages={1511-1516},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - New and Used Bills Classification Using Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1511
EP - 1516
AU - Dongshik KANG
AU - Sigeru OMATU
AU - Michifumi YOSHIOKA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1999
AB - 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.
ER -