Speech Signal Denoising Using Wavelet Transform in MATLAB

Speech signal denoising is a fundamental signal processing technique commonly implemented using wavelet transform methods. Within the MATLAB development environment, this process involves several key steps: first performing wavelet decomposition on the noisy speech signal using functions like wavedec() to obtain wavelet coefficients across different frequency bands. The core algorithm applies thresholding techniques (soft or hard thresholding) to these coefficients using threshold() or wthresh() functions, where noise components typically manifest as smaller coefficients that can be eliminated or reduced. The denoised wavelet coefficients then undergo inverse transformation using waverec() to reconstruct the enhanced speech signal in the time domain. This wavelet-based approach effectively separates noise from speech components, resulting in clearer audio quality with improved signal-to-noise ratio. The method significantly enhances speech intelligibility and signal quality while preserving important speech characteristics through multi-resolution analysis capabilities of wavelet transforms.