| Paper ID | AUD-5.3 |
| Paper Title |
DEEP RESIDUAL ECHO SUPPRESSION WITH A TUNABLE TRADEOFF BETWEEN SIGNAL DISTORTION AND ECHO SUPPRESSION |
| Authors |
Amir Ivry, Israel Cohen, Baruch Berdugo, Technion - Israel Institute of Technology, Israel |
| 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 paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. This system embeds a design parameter that allows a tunable tradeoff between the desired-signal distortion and residual echo suppression in double-talk scenarios. The system employs 136 thousand parameters and requires 1.6 Giga floating-point operations per second and 10 Mega-bytes of memory. The implementation satisfies both the timing requirements of the AEC challenge and the computational and memory limitations of on-device applications. Experiments are conducted with 161 h of data from the AEC challenge database and from real independent recordings. We demonstrate the performance of the proposed system in real-life conditions and compare it with two competing methods regarding echo suppression and desired-signal distortion, generalization to various environments, and robustness to high echo levels. |
