Neuro-classification of Bill Fatigue Levels Based on Acoustic Wavelet Components
Neuro-classification of Bill Fatigue Levels Based on Acoustic Wavelet Components
复制标题
基于声学小波分量的票据疲劳程度的神经分类
DOI：
10.1007/3-540-46084-5_174
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
T. Kosaka
中科院分区：
文献类型：
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作者：
M. Teranishi;S. Omatu;T. Kosaka
本文提出了一种新的方法来分类票据（纸币）到不同的疲劳程度，由于其损坏的程度。当钞票通过银行机时，从钞票发出特征声学信号。为了将声学信号分为三个票据疲劳水平，我们计算声学小波功率模式作为学习矢量量化（LVQ）算法的竞争神经网络的输入。实验结果表明，该方法可以获得更好的分类性能比传统的基于声信号的分类方法。因此，LVQ算法表现出良好的分类效果。
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 networkwith 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.