@article{cutler2023icassp,
author = {Cutler, Ross and Saabas, Ando and Parnamaa, Tanel and Purin, Marju and Indenbom, Evgenii and Ristea, Nicolae Catalin and Guzvin, Jegor and Gamper, Hannes and Braun, Sebastian and Aichner, Robert},
title = {ICASSP 2023 Acoustic Echo Cancellation Challenge},
year = {2023},
month = {September},
abstract = {The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20 ms, as well as including a full-band version of AECMOS (Purin et al., 2020). 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 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework and provide an objective metric for researchers to quickly test their results. The winners of this challenge were selected based on the average mean opinion score (MOS) achieved across all scenarios and the word accuracy (WAcc) rate.},
url = {http://approjects.co.za/?big=en-us/research/publication/icassp-2023-acoustic-echo-cancellation-challenge/},
pages = {675-685},
journal = {IEEE Open Journal of Signal Processing},
volume = {5},
}