An Echo Suppression Technique Utilizing an Individually Activated Factor-Adjusted Proportionally Fast Normalized LMS Algorithm

Adaptive algorithm is one of the most popular in the application of acoustic echo cancellation(AEC). LMS algorithm is widely used because of its low complexity and fast convergence rate of RLS algorithm. In sparse and dispersive systems, the proportional fast normalized least mean square algorithm (PFNLMS) with sparse control has the best performance. In this paper, in order to enhance the performance of the original PNLMS algorithm and solve the problem of slow convergence during the updating of small coefficients, the Individual Activation factor (IAF) -MPNLMS algorithm is used to solve this problem by assigning a single activation factor to the coefficients. The IAF-MPFNLMS algorithm is proposed based on the fast convergence rate of FNLMS algorithm. Compared with conventional filters, it has advantages in the convergence speed, tracking ability and steady-state mean square error (MSE) of sparse and dispersive systems. Simulation results are given to verify the effectiveness of the proposed algorithm.