適応ディジタルフィルタとニューラルネットワークを併用した騒音環境下における紙幣音響データの雑音除去

概要

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Recently, failure detection by acoustic data is applied to quality check such as new or used bill classification. However, it is difficult to reduce the noise in acoustic data of bill by conventional techniques since there are many types of noises in the environment. In this paper, we propose a method by using an adaptive digital filter and a neural network to reduce noises in acoustic data of bill. First, the acoustic data is divided into some frequency bands by the wavelet transform. Next, each data is passed through two types of filters to reduce the noise. One is an adaptive digital filter trained by the LMS algorithm, and the other is a nonlinear filter based on a neural network trained by the error back-propagation method. Then the processed data in each frequency band are composed to get an estimated signal. Finally, simulation results are illustrated to show the effectiveness of the proposed method compared with conventional approaches.
2009-09-01