A new variable step size Least Mean Square (LMS) FIR adaptive filter algorithm (VSS-CC) is proposed. In the VSS-CC algorithm the step size adjustment (α) is controlled by using the correlation between the output error (e(n)) and the adaptive filter output ((n)). At small times, e(n) and (n) are correlated which will cause a large α providing faster tracking. When the algorithm converges, the correlation will result in a small size α to yield smaller misadjustments. Computer simulations show that the proposed VSS-CC algori thm achieves a better Echo Return Loss Enhancemen (ERLE) than a conventional NLMS Algorithm. The VSS-CC algorithm was also compared with another variable step algorithm, achieving the VSS-CC a better ERLE when the additive noise is incremented.

This letter proposes a time varying step size normalized LMS (TVS-NLMS) algorithm for adaptive echo canceler structures. Proposed algorithm reduces distortion during double talk, without increasing the computational cost nor decreasing the convergence rate of the normalized LMS algorithm significantly. Simulation results using white noise and actual speech signals confirm the desirable features of the proposed scheme.