Multiple sub-filter using variable step size and partial update for acoustic echo cancellation

Abstract

In this paper a M-max partial update and variable step size normalized least mean square (M-max VSS NLMS) algorithm for multiple sub-filter (MSF) based Acoustic echo cancellation has been studied. Here computational complexity is controlled by M-max partial update approach where fast convergence is achieved by variable step size NLMS algorithm with MSF parallel structure. Further the condition for convergence analysis has been derived for MSF using M-max VSS NLMS algorithm. Simulation results shows that M-max partial update coefficients with VSS NLMS algorithm for MSF has fast convergence as compared to the single long filter (SLF) and all other combinations. As expected the convergence rate of all the algorithms is fast when the input is a sample function of white random process as compared to that of correlated signal.