Pistachio Classification Based on Acoustic Systems and Machine Learning - Yavuz Türkay; Zekiye Seyma Tamay (Elektronika ir Elektrotechnika, 2024)
An acoustic emission and machine learning based pistachio classification system has been developed. This system performs feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using support vector machine (SVM). This study revealed that when closed-shelled pistachios hit a steel plate, they have different frequency components compared to open-shelled pistachios. The audio signals of the samples selected for the classification process were recorded using a high sensitivity carbon microphone and MATLAB Analog Input Recorder. These recorded sounds were processed by applying a hamming window to remove ambient noise and make them more clearly analyzable.