Speech Signal Denoising Using Wavelet Transform

This project implements a MATLAB program that employs wavelet transform techniques for speech signal denoising. The program effectively reduces noise interference in speech signals, enhancing audio clarity and intelligibility. Wavelet transform represents a powerful mathematical tool widely used in signal processing applications. By performing wavelet decomposition on input signals, the program separates signals into frequency components at different scales, enabling precise noise removal through thresholding operations. The implementation involves key MATLAB functions including wavedec for multi-level wavelet decomposition, wthresh for applying thresholding rules (hard or soft thresholding), and waverec for signal reconstruction. The algorithm typically follows these steps: pre-processing the audio signal, selecting appropriate wavelet basis functions (such as Daubechies or Symlets), determining optimal decomposition levels, applying thresholding to wavelet coefficients, and reconstructing the denoised signal. Beyond speech signal applications, this program can be extended to other domains including image processing and data compression. Through learning and utilizing this implementation, users can master fundamental wavelet transform principles and practical application techniques, providing a solid foundation for further research and development in related fields. The code includes configurable parameters for wavelet selection, threshold calculation methods (universal or minimax), and decomposition levels to accommodate different signal characteristics.