Çetin, A. EnisPearson, T. C.Tewfik, A. H.2016-02-082016-02-082004-05http://hdl.handle.net/11693/27449Date of Conference: 17-21 May 2004Conference name: 2004 IEEE International Conference on Acoustics, Speech, and Signal ProcessingAn 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.EnglishImpact acousticsPistachio nutsPrincipal component analysis (PCA)Speech dataAcousticsAlgorithmsData acquisitionEigenvalues and eigenfunctionsFrequenciesSignal processingThroughputVectorsWavelet transformsFood productsConference Paper10.1109/ICASSP.2004.1327201