Wavelet Transform-based Variable Step Size Adaptive LMS Algorithm for Speech Signal Denoising

This program implements a wavelet transform-based variable step size adaptive LMS algorithm for speech signal denoising, achieving remarkable effectiveness against complex real-world noise. The algorithm leverages wavelet decomposition principles to automatically adjust the step size parameter according to different speech signal characteristics. Key implementation features include multi-level wavelet decomposition for noise separation, adaptive step size control based on signal-to-noise ratio estimation, and LMS filtering with real-time coefficient updates. The system effectively eliminates complex noise components while preserving speech quality, significantly enhancing signal clarity and intelligibility. The code structure incorporates modular components for wavelet analysis, adaptive filtering, and threshold optimization, providing a robust solution for practical noise reduction in various real-world scenarios. This implementation offers a reliable approach for speech signal processing applications, featuring automatic parameter adjustment and efficient computational performance.