An adaptive filter with gain and time-shift parameters for echo cancellation
In this paper, we propose a simple yet effective parametric adaptive filter (PAF) and demonstrate its excellent performance in acoustic echo cancellation applications. Specifically, the proposed PAF decomposes an adaptive filter into three components: a normalized finite impulse response filter, a gain factor and a time-shift factor. With a novel update scheme for the PAF, these three components can be adjusted with different step-size respectively. Therefore, the novel adaptive filter with a parametric structure is suitable to track an acoustic echo path, the change of which mainly happens to the gain and time-shift while the 'basic shape' of the transfer function keeps relatively stable. It is also important to point out that the proposed PAF is a general platform of adaptive filtering, and the conventional method can be considered as a special case of this general platform. Experiments based on measured impulse response in realistic environment show that the proposed PAF outperforms the conventional method on convergence rate in echo cancellation application when dealing with channel changes caused by adjustments of system gain and delay.
& Wu, Z.
(2017). An adaptive filter with gain and time-shift parameters for echo cancellation. Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016.