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 (ERLE) and perceptual evaluation of speech quality (PESQ) 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 two large test sets, and 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.

中文翻译：

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ICASSP 2021 声学回声消除挑战：数据集、测试框架和结果

ICASSP 2021 声学回声消除挑战旨在激发声学回声消除 (AEC) 领域的研究，这是语音增强的重要组成部分，并且仍然是音频通信和会议系统中的首要问题。许多最近的 AEC 研究报告了在合成数据集上的良好性能，其中训练和测试样本来自相同的基础分布。但是，AEC 性能在实际录音中通常会显着降低。此外，大多数传统的客观指标，例如回声回波损耗增强 (ERLE) 和语音质量感知评估 (PESQ)，在现实环境中存在背景噪声和混响的情况下，与主观语音质量测试的相关性不佳。在这个挑战中，我们开源了两个大型数据集，用于在单方通话和双方通话场景下训练 AEC 模型。这些数据集包括来自 2,500 多个真实音频设备和真实环境中的人类扬声器的录音，以及一个合成数据集。我们开源了两个大型测试集，并开源了一个在线主观测试框架，供研究人员快速测试他们的结果。本次挑战赛的获胜者将根据在所有不同的单方通话和双方通话场景中获得的平均意见得分 (MOS) 选出。我们开源了一个在线主观测试框架，供研究人员快速测试他们的结果。本次挑战赛的获胜者将根据在所有不同的单方通话和双方通话场景中获得的平均意见得分 (MOS) 选出。我们开源了一个在线主观测试框架，供研究人员快速测试他们的结果。本次挑战赛的获胜者将根据在所有不同的单方通话和双方通话场景中获得的平均意见得分 (MOS) 选出。