| Paper: | MLSP-P4.3 | ||
| Session: | Machine Learning Applications | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Other Applications | ||
| Title: | CLASSIFICATION OF CLOSED AND OPEN SHELL PISTACHIO NUTS USING PRINCIPAL COMPONENT ANALYSIS OF IMPACT ACOUSTICS | ||
| Authors: | Enis Çetin; Bilkent University | ||
| Tom Pearson; United States Department of Agriculture | |||
| Ahmed Tewfik; University of Minnesota | |||
| 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. | ||
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