Enhanced VLSI Speech Enhancement Systems Utilizing Adaptive LMS Filters and Machine Learning Techniques for Echo Cancellation

The project “Enhanced VLSI Speech Enhancement Systems Utilizing Adaptive LMS Filters and Machine Learning Techniques for Echo Cancellation” integrates advanced signal processing and machine learning to improve speech quality in Very Large-Scale Integration (VLSI) environments. The cornerstone of this system is the innovative use of a Convolutional Neural Network (CNN) to dynamically predict the parameters for an adaptive Least Mean Squares (LMS) filter, which is then simulated in a Verilog environment. The methodology begins in a Matlab environment, where audio processing libraries are used to collect and preprocess a vast array of audio data, creating a diverse training set. A CNN model is meticulously developed to interpret this data and forecast optimal filter settings. These predicted parameters are then translated into a hex file format, compatible with the ModelSim environment for hardware simulation. In parallel, a Verilog model of the LMS filter is crafted, designed to adapt its parameters in real-time, simulating echo cancellation within a VLSI context. The hex audio files are processed through the filter in ModelSim, with the CNN-derived parameters guiding the adaptive filtering process. Post-simulation, the output is converted back to MP3 format using a Matlab script for practical evaluation of the system's performance. The project showcases a unique synergy between software intelligence and hardware functionality, culminating in a highly adaptive system capable of real-time speech enhancement. This system not only achieves superior echo cancellation but also lays the groundwork for sophisticated, real-world VLSI applications where audio clarity is paramount.