International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Analog-to-digital conversion systems face critical challenges, including noise interference, signal degradation, and limited adaptability in varying environmental conditions. This research introduces a machine learning-integrated conversion and classification system that transforms how we process audio signals in the digital era. Our intelligent conversion model employs a three-level quantization approach (-1, 0, 1) with user-defined thresholds, seamlessly integrated with logistic regression classification for enhanced pattern recognition. The system dynamically adapts between operational modes based on signal amplitude characteristics, achieving superior performance, 99.9% classification accuracy - demonstrating exceptional signal interpretation capability, CD-quality audio processing at 44.1 kHz sampling rate with minimal distortion, strong noise immunity with SNR of 31.6 dB and THD+N of -31.6 dB, and Real-time adaptive processing through intelligent threshold-based categorization. The study adopted Agile Methodology, implemented using MATLAB and validated through a comprehensive confusion matrix analysis. This system represents a standard shift from traditional signal processing to intelligent, self-adapting conversion technology. This study bridges the gap between standard signal processing and modern machine learning, providing a scalable solution for next-generation digital communication systems that require high fidelity and intelligent adaptability, and the modular architecture allows each processing phase to be individually tested and optimized, making it appropriate for telecommunications, industrial automation, and consumer electronics applications.

Keywords: Analog Signal, Machine Learning, Digital Signal, Signal Processing, Logistic Regression, Audio Classification, Adaptive Conversion

Downloads: | DOI: 10.17148/IJARCCE.2025.14903