Identification of Drug-addicted People using Short Length of Voice Signal through Haar and Symlet Wavelet Transform

Recognizing and classifying signals is one of the most significant tasks nowadays. For an uncountable number of purposes, classification, pattern recognition, data pre-processing, and prediction science are used worldwide. In this work, our objective is to understand, analyze, visualize, recognize, and identify drug-addicted and non-addicted people by using their short length of voice signals through Haar and Symlet (Sym2) wavelet transform. Here, we used signals of speech at a considerable length to achieve our goal and provide opportunities for the law-and-order enforcing authority and the people who are interested in this area. We visualize each signal and analyze them using different wavelet transform to understand the similarities and dissimilarities between the voice signals. After wavelet transform, we calculate the PSNR and SNR values of the voice signals using MATLAB wavelet toolbox. To the PSNR and SNR values of the voice signals and try to make the similarities and dissimilarities between the voice signals. From the values we can make a decision to identifying a Drug-addicted people.