Kalman Filter Implementation for Speech Signal Enhancement with Colored Noise

This documentation details the MATLAB code implementation process to enhance readers' understanding of the algorithm. For speech signal enhancement, we employ Kalman filtering technology - a sophisticated algorithm capable of signal prediction and estimation that has been widely adopted in speech processing applications. The implementation involves defining state-space models and configuring Kalman filter parameters through functions like 'kalman' or custom implementations using prediction and correction steps. To simulate realistic noise conditions, we introduce colored noise containing signals of different frequencies, which better mimics real-world acoustic environments compared to white noise. In our experiments using MATLAB's signal processing toolbox, this approach demonstrates exceptional performance by effectively improving speech signal quality and clarity, enabling easier comprehension and recognition of speech content. The code structure typically includes noise generation using filtered random processes, signal preprocessing, and iterative Kalman filter application. Therefore, we consider this method a highly promising speech signal processing technique worthy of further exploration and research.