Speech Denoising Implementation Using Wiener Filtering, Spectral Subtraction, and Adaptive Filtering Methods

This project provides MATLAB source code implementing speech denoising through three distinct algorithms: Wiener filtering, spectral subtraction, and adaptive filtering methods. The implementation includes a comprehensive GUI interface that allows users to adjust critical parameters such as filter coefficients, noise estimation thresholds, and adaptation rates. The core algorithms are implemented using MATLAB's signal processing toolbox functions, including spectral analysis using FFT, noise power estimation, and real-time filter adaptation mechanisms. To enhance the program's practicality, several additional features could be implemented. An automatic noise level detection function could be added using statistical analysis of audio segments, enabling the system to dynamically adjust denoising parameters based on varying noise environments. Real-time audio waveform and spectrum visualization capabilities could be integrated using MATLAB's plotting functions, allowing users to visually observe denoising effects through before-and-after comparisons. Algorithm performance optimization could focus on computational efficiency improvements, such as implementing overlap-add methods for frame-based processing and optimizing matrix operations for faster execution. Finally, user-friendly features like tooltip guidance, input validation, and comprehensive error handling could be implemented using MATLAB's dialog boxes and exception handling to enhance user experience and minimize operational errors.