MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, GUARANTEES OR CONDITIONS WITH RESPECT TO YOUR USE OF THE DATASETS. MICROSOFT PROVIDES THE DATASETS ON AN "AS IS" BASIS. Then, set up Git LFS for your user account by cutler2021crowdsourcing, First, download and install the Git LFS client. Set up Git Large File Storage (LFS) for faster download of the datasets.Links to newest fullband real-world recordings are located at and.Test set for the ICASSP 2022 challenge is located at.The datasets directory contains the real and synthetic training datasets and real test sets.The winners of this challenge are selected based on the average Mean Opinion Score achieved across all different single talk and double talk scenarios, and the speech recognition rate.įor more details about the challenge, please visit the challenge website and refer to the paper. We also open source an online subjective test framework and provide an online objective metric service for researchers to quickly test their results. These datasets consist of recordings from more than 7,500 real audio devices and human speakers in real environments, as well as a synthetic dataset. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. This is the third AEC challenge and it is enhanced by including mobile scenarios, adding speech recognition rate in the challenge goal metrics, and making the default sample rate 48 kHz. The ICASSP 2022 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.
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