Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics
Author
Çetin, A. Enis
Pearson, T. C.
Tewfik, A. H.
Date
2004-05Source Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher
IEEE
Pages
677 - 680
Language
English
Type
Conference PaperItem Usage Stats
146
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Abstract
An algorithm was developed to separate pistachio nuts with closed-shells from those with open-shells. It was observed that upon impact on a steel plate, nuts with closed-shells emit different sounds than nuts with open-shells. Two feature vectors extracted from the sound signals were melcepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed-shells and open-shells were used to obtain a representative vector of each class. The accuracy of closed-shell nuts was more than 99% on the test set.
Keywords
Impact acousticsPistachio nuts
Principal component analysis (PCA)
Speech data
Acoustics
Algorithms
Data acquisition
Eigenvalues and eigenfunctions
Frequencies
Signal processing
Throughput
Vectors
Wavelet transforms
Food products