A Wavelet-Based Approach to Rapidly Identify Drug-Addicted Individuals Using Voice Signal Analysis

Sajeeb Hassan¹, Sadia Afrin^2*, Md.Rafiqul Islam¹

Wavelet transforms analyze voice signals to distinguish addiction, aiding intervention strategies and enhancing law enforcement's ability to recognize behavioral patterns effectively. 

Abstract

Recognizing and classifying signals plays a pivotal role across diverse disciplines such as classification, pattern recognition, data preprocessing, and predictive science. This study specifically investigates the use of Haar and Symlet (Sym2) wavelet transforms in analyzing short voice signals to distinguish between drug-addicted and non-addicted individuals. The primary aim is to uncover distinctive features in voice signals that correlate with addiction. Haar and Symlet wavelet transforms are employed to process substantial segments of speech signals, extracting meaningful insights that can aid law enforcement agencies and addiction researchers. Each signal undergoes visualization and analysis to unveil patterns and discrepancies brought to light by different wavelet transformations. Following the wavelet transformation process, the study evaluates the Peak Signal-to-Noise Ratio (PSNR) and Signal-to-Noise Ratio (SNR) using MATLAB's wavelet toolbox. These metrics serve as crucial indicators for decision-making processes aimed at identifying drug-addicted individuals based on unique voice signal characteristics. Beyond advancing signal processing techniques, this research aims to have practical applications in recognizing behavioral patterns associated with addiction. Ultimately, the goal is to leverage these insights to develop more effective intervention and support strategies within relevant communities and healthcare settings. By integrating wavelet analysis with voice signal processing, this study not only enhances our understanding of addiction-related behaviors but also contributes valuable tools for real-world applications in law enforcement and healthcare sectors. These efforts are geared towards fostering more targeted and efficient responses to addiction issues in society.

Keywords: Wavelet Transform, Voice Signal Analysis, Addiction Detection, Signal Processing, Behavioral Patterns

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