Application of Classical Adaptive Filters in Speech Enhancement

In many applications of noise cancellation the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least mean square (LMS) and Normalized LMS (NLMS) adaptive filters have been used in a wide range of signal processing applications because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the “ultimate” adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of this algorithm are often associated with high computational complexity and/or poor numerical properties. In this paper we have performed and compared these classical adaptive filters for attenuating noise in speech signals. In each algorithm, the optimum order of filter of adaptive algorithms have also been found through experiments.

Keywords:

Adaptive Filter , Least Mean Squares , Recursive Least Squares , Noise Cancellation