Wind Sounds Classification Using Different Audio Feature Extraction Techniques

Wala'a Nsaif Jasim, Saba Abdual Wahid Saddam, Esra'a Jasem Harfash


   In this research, Different audio feature extraction technique are implemented and classification approaches are presented to classify seven types of wind. Where we applied features technique such as Zero Crossing Rate (ZCR) ,Fast Fourier Transformation (FFT), Linear predictive coding (LPC), Perceptual Linear Prediction (PLP). We know that some of these methods are good with human voices, but we tried to apply them here to characterize the wind audio content. The CNN classification method is implemented to determine the class of input wind sound signal. Experimental results show that each of these extraction feature methods are gave different results, but classification accuracy that are obtained of PLP features proven to have the best results.

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