| Paper ID | AUD-5.2 |
| Paper Title |
COMBINING ADAPTIVE FILTERING AND COMPLEX-VALUED DEEP POSTFILTERING FOR ACOUSTIC ECHO CANCELLATION |
| Authors |
Mhd Modar Halimeh, Thomas Haubner, Annika Briegleb, Alexander Schmidt, Walter Kellermann, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany |
| Session | AUD-5: Active Noise Control, Echo Reduction, and Feedback Reduction 1: Echo Cancellation |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-NEFR] Active Noise Control, Echo Reduction and Feedback Reduction |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 postfilter (PF). Due to its complex-valued nature, the PF allows to suppress 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. |
