Abstract
This paper proposes a new method to classify bills(paper moneys) into different fatigue levels due to the extent of their damage. While a bill passing through a banking machine, a characteristic acoustic signal is emitted from the bill. To classify the acoustic signal into three bill fatigue levels, we calculate the acoustic wavelet power pattern as the input to a competitive neural network with the Learning Vector Quantization(LVQ) algorithm. The experimental results show that the proposed method can obtain better classification performance than the best of conventional acoustic signal based classification methods. It is, consequently, the LVQ algorithm demonstrates a good classification.
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References
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Teranishi, M.,Omatu, S.,Kosaka, T.: New and Used Bill Money Classification Using Spectral Information Based on Acoustic Data of Banking Machine, Trans. IEE of Japan, Vol.117-C,11 (1997) 1677–1681
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Teranishi, M., Omatu, S., Kosaka, T.: Classification of Three Fatigue Levels for Bills Using Acoustic Frequency Band Energy Patterns, Trans. IEE of Japan, Vol.120-C,11(2000) 1602–1608
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© 2002 Springer-Verlag Berlin Heidelberg
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Teranishi, M., Omatu, S., Kosaka, T. (2002). Neuro- classification of Bill Fatigue Levels Based on Acoustic Wavelet Components. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_174
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DOI: https://doi.org/10.1007/3-540-46084-5_174
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Online ISBN: 978-3-540-46084-8
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