A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation

Autor:	Thomas Haubner, Mhd. Modar Halimeh, Andreas Brendel, Walter Kellermann
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	FOS: Computer and information sciences Sound (cs.SD) Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Popis:	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. Accepted for European Signal Processing Conference (EUSIPCO), Dublin, Ireland, August 2021
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aaa90d83c08a9d0aa6f5bda5dd579878 http://arxiv.org/abs/2012.08867