A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation
This work provides an incremental improvement for acoustic echo cancellation systems, specifically benefiting telecommunication and voice interaction applications where echo path changes are common.
This paper addresses the challenge of acoustic echo cancellation, particularly in scenarios with abrupt echo path changes. It proposes a synergistic approach combining adaptive Kalman filtering with a deep neural network-based postfilter, achieving rapid reconvergence after echo path changes without sacrificing steady-state performance.
We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter. The proposed algorithm overcomes the well-known limitations of Kalman filter-based adaptation control in scenarios characterized by abrupt echo path changes. As the key innovation, we suggest to exploit the different statistical properties of the interfering signal components for robustly estimating the adaptation step size. This is achieved by leveraging the postfilter near-end estimate and the estimation error of the Kalman filter. The proposed synergistic scheme allows for rapid reconvergence of the adaptive filter after abrupt echo path changes without compromising the steady state performance achieved by state-of-the-art approaches in static scenarios.