Combining Adaptive Filtering And Complex-Valued Deep Postfiltering For Acoustic Echo Cancellation
Mhd Modar Halimeh, Thomas Haubner, Annika Briegleb, Alexander Schmidt, Walter Kellermann
Abstract
In this contribution, we introduce a novel approach to noise-robust acoustic echo cancellation employing a complex-valued Deep Neural Network (DNN) for postfiltering. In a first step, early linear echo components are removed using a double-talk robust adaptive filter. The residual signal is subsequently processed by the proposed post-filter (PF). Due to its complex-valued nature, the PF allows to sup-press unwanted signal components without introducing distortions to the near-end speaker. For training and evaluation, we exclusively use data from the ICASSP 2021 AEC challenge. Exploiting only a moderate amount of training data, we demonstrate the efficacy of the proposed method. Specifically, we show that the PF (i) benefits significantly from a preceding linear adaptive filter and (ii) significantly outperforms a conventional real-valued DNN-based PF.