Çetin, A. EnisPearson, T. C.Tewfik, A. H.2016-02-082016-02-0820040001-2351http://hdl.handle.net/11693/24307An algorithm using speech recognition technology was developed to distinguish pistachio nuts with closed shells from those with open shells. It was observed that upon impact with a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Features extracted from the sound signals consisted of mel-cepstrum coefficients and eigenvalues obtained from the principle component analysis (PCA) of the autocorrelation matrix of the sound signals. Classification of a sound signal was performed by linearly combining the mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable, as are most speech-recognition algorithms. During the training phase, sounds of nuts with closed shells and with open shells were used to obtain a representative vector of each class. During the recognition phase, the feature vector from the sample under question was compared with representative vectors. The classification accuracy of closed-shell nuts was more than 99% on the validation set, which did not include the training set.EnglishAcousticPistachioRecognitionSoundAcoustic wavesAlgorithmsFeature extractionPlates (structural components)Vector quantizationClosed shellsPistachio nutsSpeech recognitionalgorithmfood productionnutPistacia veraArticle