Acoustic Echo Cancellation Using NLMS Algorithm Implementation

This project implements acoustic echo cancellation using the Normalized Least Mean Square (NLMS) algorithm. The implementation involves four critical components: the original speech signal, echo-corrupted speech signal, NLMS algorithm implementation, and echo path modeling.

The Normalized Least Mean Square (NLMS) algorithm is an adaptive filtering technique that continuously adjusts filter coefficients to minimize echo effects in speech signals. Implementation typically involves initializing filter weights, calculating the error signal between desired and actual output, and updating coefficients using a normalized step size for stable convergence. This algorithm finds extensive applications in speech processing, particularly in voice communication systems and speech recognition technologies where real-time echo cancellation is crucial.

The echo path represents the acoustic transmission route from sound source to receiver, incorporating potential reflections and delays that cause echoes. Accurate echo path estimation is fundamental to effective echo cancellation, often implemented through adaptive filter modeling that simulates the actual acoustic environment. The implementation typically uses FIR filters with adjustable coefficients that adapt to changing acoustic conditions.

For practical implementation, key functions include signal preprocessing, frame-based processing for real-time applications, and step-size normalization to maintain stability across varying signal amplitudes. The algorithm's efficiency makes it suitable for embedded systems and real-time communication platforms.

Should you have any technical questions regarding the implementation details or algorithm optimization approaches, please feel free to inquire for further clarification.