Non-linear acoustic echo cancellation using cascaded Kalman filtering

This paper presents a new paradigm of solving the non-linear acoustic echo cancellation problem. The non-linear echo path is modeled by a memoryless non-linearity followed by a linear FIR filter. The problem is cast into a state-space framework and solved using a cascade of Kalman filters in time domain, one filter adapting to the linear echo path and the other filter adapting to the memoryless non-linearity. It is shown that the proposed method outperforms the existing NLMS-based method in filter convergence and misalignment while enjoying an additional benefit of unsupervised and variable step-size control. Interesting connections will be made between the proposed method and the widely-known NLMS-based echo canceler. Practical recommendations are provided on implementing the proposed method efficiently on a general-purpose processor. Finally, simulation results are presented that exhibit its performance advantages.