MATLAB Code Implementation for Speech Denoising

This document discusses an M-program for speech denoising that involves adding noise to an original signal and applying filtering techniques to recover the clean audio. The implementation typically begins by loading or generating a clean speech signal, then artificially adding Gaussian or environmental noise to simulate real-world conditions. Key filtering approaches may include Wiener filtering, spectral subtraction, or adaptive filters like LMS (Least Mean Squares) algorithms. We can further explore how to select appropriate filtering methods based on signal-to-noise ratio (SNR) requirements and computational constraints. Algorithm optimization techniques may involve parameter tuning for filter coefficients, frame size selection for short-time Fourier transform (STFT) processing, and implementing real-time processing capabilities. The denoising algorithm finds applications in various fields including voice communication systems, audio processing software, hearing aid devices, and speech recognition systems. These discussions help deepen our understanding of speech denoising technology and its practical implementations, particularly focusing on MATLAB's signal processing toolbox functions such as filter(), audioread(), and spectral analysis tools for effective noise reduction.