| Paper ID | SPE-52.2 |
| Paper Title |
LOW-COMPLEXITY, REAL-TIME JOINT NEURAL ECHO CONTROL AND SPEECH ENHANCEMENT BASED ON PERCEPNET |
| Authors |
Jean-Marc Valin, Srikanth Tenneti, Karim Helwani, Umut Isik, Arvindh Krishnaswamy, Amazon, Canada |
| Session | SPE-52: Speech Enhancement 8: Echo Cancellation and Other Tasks |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-ENHA] Speech Enhancement and Separation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Speech enhancement algorithms based on deep learning have greatly surpassed their traditional counterparts and are now being considered for the task of removing acoustic echo from hands-free communication systems. This is a challenging problem due to both real-world constraints like loudspeaker non-linearities, and to limited compute capabilities in some communication systems. In this work, we propose a system combining a traditional acoustic echo canceller, and a low-complexity joint residual echo and noise suppressor based on a hybrid signal processing/deep neural network (DSP/DNN) approach. We show that the proposed system outperforms both traditional and other neural approaches, while requiring only 5.5% CPU for real-time operation. We further show that the system can scale to even lower complexity levels. |
