Speech Enhancement Using Improved LMS and RLS Algorithms

This document provides a complete MATLAB implementation for speech enhancement utilizing improved versions of both LMS (Least Mean Squares) and RLS (Recursive Least Squares) algorithms. The program includes comprehensive functionality for handling input/output sequences and reading audio data files, along with the core adaptive enhancement algorithm implementation. The implementation features several key components: data preprocessing modules for signal input and format conversion, adaptive filter structures for noise cancellation, and real-time processing capabilities for streaming audio applications. The improved algorithms incorporate convergence acceleration techniques and stability enhancements over standard LMS and RLS implementations. We provide detailed explanations of each processing stage, accompanied by relevant code examples and algorithm descriptions. The MATLAB code includes functions for parameter initialization, filter coefficient adaptation, and performance evaluation metrics. Users can modify algorithm parameters and adjust processing blocks according to specific application requirements to achieve optimal speech enhancement results. Key implementation aspects include: frame-based processing for efficient memory usage, overlap-add methods for seamless signal reconstruction, and adaptive step-size control for robust performance across varying noise conditions. The code structure follows modular design principles, allowing easy integration with existing audio processing pipelines. This implementation serves as a practical reference for researchers and engineers working on adaptive filtering applications in speech processing, providing both theoretical foundations and practical implementation details for effective noise reduction in speech signals.