Wavelet Transform for Speech Signal Processing

This MATLAB program performs speech denoising through wavelet transform processing of speech signals. The implementation employs discrete wavelet transform (DWT) to decompose speech signals into different frequency subbands, enabling effective noise reduction by applying thresholding techniques to wavelet coefficients. The algorithm typically involves selecting appropriate wavelet families (such as Daubechies or Symlets), determining optimal decomposition levels, and implementing soft or hard thresholding methods to suppress noise components while preserving speech characteristics. Key functions include wavelet decomposition using wavedec, coefficient thresholding with wthresh, and signal reconstruction through waverec. This signal processing approach enhances speech quality and clarity by separating speech components from noise across different frequency bands. Users can efficiently remove background noise from speech recordings, resulting in cleaner and more intelligible audio outputs through this wavelet-based denoising methodology.