| Paper ID | AUD-6.2 |
| Paper Title |
ICASSP 2021 ACOUSTIC ECHO CANCELLATION CHALLENGE: DATASETS, TESTING FRAMEWORK, AND RESULTS |
| Authors |
Kusha Sridhar, University of Texas at Dallas, United States; Ross Cutler, Ando Saabas, Tanel Parnamaa, Markus Loide, Hannes Gamper, Sebastian Braun, Robert Aichner, Sriram Srinivasan, Microsoft, United States |
| Session | AUD-6: Active Noise Control, Echo Reduction, and Feedback Reduction 2: Active Noise Control and 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 |
The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report good performance on synthetic datasets where the train and test samples come from the same underlying distribution. However, the AEC performance often degrades significantly on real recordings. Also, most of the conventional objective metrics such as echo return loss enhancement and perceptual evaluation of speech quality do not correlate well with subjective speech quality tests in the presence of background noise and reverberation found in realistic environments. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 2,500 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework for researchers to quickly test their results. The winners of this challenge will be selected based on the average Mean Opinion Score (MOS) achieved across all different single talk and double talk scenarios. |
