Classification of New and Used Bills Using Acoustic Cepstrum of a Banking Machine by Neural Networks
Classification of New and Used Bills Using Acoustic Cepstrum of a Banking Machine by Neural Networks
复制标题
通过神经网络使用银行机的声学倒谱对新钞和旧钞进行分类
DOI：
10.1541/ieejeiss1987.119.8-9_955
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发表时间：
1999
影响因子：
--
通讯作者：
T. Kosaka
中科院分区：
文献类型：
--
作者：
M. Teranishi;S. Omatu;T. Kosaka
提出了一种利用神经网络的声学倒谱模式对新旧钞票进行分类的方法。所提出的方法处理纸币通过银行机产生的声学信号。通过使用声学倒谱模式，可以将声谱的粗略结构表示为具有比频谱更小的模式尺寸的倒谱。该方法采用神经网络作为分类器。两种不同类型的神经网络，一种是三层感知器网络，另一种是竞争神经网络，用来评价哪种类型的神经网络更适合于倒谱模式的分类。实验结果表明了该方法的有效性，竞争神经网络比三层感知器具有更好的分类性能。
This paper proposes a method to classify new and used bills using the acoustic cepstrum pattern by neural networks. The proposed method deals with an acoustic signal which has been generated by the bill passing through a banking machine. By using an acoustic cepstrum pattern, the rough structure of the acoustic spectrum can be represented as the cepstrum with a smaller pattern size than the spectrum. The proposed method employs a neural network as the classifier. Two different types of the neural network, one is the three layered perceptron and the other is the competitive neural network, are used to evaluate which type is more suitable for classification of the cepstrum pattern. The experimental results show the effectiveness of the proposed method, and that the competitive neural network yields better classification performance than the three-layered perceptron.