Implementation of Speech Enhancement

This article presents a custom-developed GUI interface designed for speech enhancement applications. To execute the program, users can navigate to the "Run" option within the "Debug" menu in the main.c file. The implementation provides three distinct speech enhancement techniques: spectral subtraction, Least Mean Squares (LMS) adaptive filtering, and Wiener filtering.

For spectral subtraction enhancement, the algorithm computes the power spectral difference between noisy and clean speech references to suppress background noise. The LMS filter implementation employs gradient descent optimization to iteratively adjust filter coefficients, minimizing mean square error for effective noise cancellation. The Wiener filter approach enhances speech quality by statistically minimizing the mean square error between original and processed signals through frequency-domain optimization.

The code structure incorporates modular design with separate functions for each algorithm: spectral_subtraction() handles frequency-domain power spectrum manipulation, lms_filter() implements the adaptive weight update mechanism using μ-step size parameter, and wiener_filter() applies statistical estimation techniques. All algorithms include real-time processing capabilities with buffer management for continuous audio stream handling.

This comprehensive solution enables users to effectively process speech signals, significantly improving audio quality and user experience in noisy environments.