Speech Signal Denoising and Voiced/Unvoiced Detection Based on Wavelet Analysis

This research focuses on speech signal denoising and voiced/unvoiced discrimination using wavelet analysis methodology. The implementation involves decomposing speech signals into different frequency subbands through multi-resolution analysis, where noise components are effectively reduced using threshold-based techniques such as soft/hard thresholding applied to wavelet coefficients. The algorithm identifies voiced segments (characterized by periodic vibration with fundamental frequency) and unvoiced segments (aperiodic noise-like components) by analyzing energy distribution patterns across wavelet decomposition levels. Key functions include wavelet transform implementation (using functions like wavedec/waverec in MATLAB), threshold calculation based on noise estimation, and feature extraction from reconstructed signals. This approach significantly enhances speech signal quality and accuracy, demonstrating substantial potential for applications in speech recognition systems and audio processing frameworks where clean feature extraction is critical.