Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics

Date
2004-05
Advisor
Instructor
Source Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
677 - 680
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
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.

Course
Other identifiers
Book Title
Keywords
Impact acoustics, Pistachio nuts, Principal component analysis (PCA), Speech data, Acoustics, Algorithms, Data acquisition, Eigenvalues and eigenfunctions, Frequencies, Signal processing, Throughput, Vectors, Wavelet transforms, Food products
Citation
Published Version (Please cite this version)