Adaptive Filtering


Adaptive filtering involves dynamically adjusting filter coefficients over time, contrasting with classical digital filters where coefficients are fixed for a specific filtering task. The key advantage of adaptive filtering lies in its ability to adapt to changing signal conditions, making it ideal for situations where the bands of interest are uncertain and depend on the signals being processed.

One prominent adaptive filtering technique is the Least Mean Squares (LMS) algorithm. This method dynamically updates filter coefficients in real-time to minimize the mean square error between the desired signal and the estimated output. LMS finds widespread applications in echo cancellation, noise reduction, channel equalization, and system identification tasks.

While the LMS algorithm is a popular and effective choice, it is important to consider other adaptive filtering approaches based on the specific requirements, convergence speed, and available prior knowledge of the system and signals. By understanding the principles of adaptive filtering, we can leverage its flexibility to tackle diverse signal processing challenges.

Mathmatic Background on LMS adaptive filter:

Here's a step-by-step explanation of the LMS algorithm Practical Implementation:

Example Active Echo Cancellation using LMS Algorithms

Following exampe simulate a speech signal with added echo, applies the LMS algorithm for active echo cancellation, and plots the original speech signal, the echo signal, and the echo-cancelled signal. You should be able to observe how the echo is successfully cancelled in the echo-cancelled signal, leaving only the original speech signal. 

See Implementation in Python Below