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| 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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