MATLAB Implementation of Wavelet Transform-Based Speech Signal Denoising Using Thresholding

This article presents a MATLAB implementation of wavelet transform-based threshold denoising for speech signals. Wavelet transform serves as a mathematical tool that decomposes signals into different frequency subbands, enabling enhanced signal analysis. The core functionality of this program involves calculating wavelet coefficients to determine appropriate thresholds for noise removal, where hard or soft thresholding techniques are applied to modify the coefficients. Key implementation aspects include multi-level wavelet decomposition using functions like wavedec, threshold estimation methods (such as universal threshold or minimax threshold), and wavelet reconstruction via waverec. The program offers parameter customization options, allowing users to adjust wavelet types, decomposition levels, and thresholding strategies to optimize denoising performance. As a significant tool in speech signal processing, this implementation effectively enhances signal quality by reducing noise while preserving important speech characteristics through careful threshold selection and wavelet coefficient processing.