Performance of Adaptive Algorithm for Noise Cancellation

In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Recursive Least square (RLS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. In this describes a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms for attenuating noise in speech signals. Three performance criteria are utilized in this study: minimum step size, the minimum mean squared error (MSE), and the required filter order. The simulation results demonstrate the good performance of the LMS, RLS, and filtered-X LMS algorithm for active noise cancellation. After simulation in MATLAB the filtered-X algorithm is implemented on the TMS320C6713 processor to see the performance by use of speaker.