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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics

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      Author
      Çetin, A. Enis
      Pearson, T. C.
      Tewfik, A. H.
      Date
      2004-05
      Source Title
      ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
      Publisher
      IEEE
      Pages
      677 - 680
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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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 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
      Permalink
      http://hdl.handle.net/11693/27449
      Published Version (Please cite this version)
      https://doi.org/10.1109/ICASSP.2004.1327201
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      • Department of Electrical and Electronics Engineering 3524
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