ICASSP 2023 Acoustic Echo Cancellation Challenge

IF 2.9 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE open journal of signal processing Pub Date : 2024-03-13 DOI:10.1109/OJSP.2024.3376289
Ross Cutler;Ando Saabas;Tanel Pärnamaa;Marju Purin;Evgenii Indenbom;Nicolae-Cătălin Ristea;Jegor Gužvin;Hannes Gamper;Sebastian Braun;Robert Aichner
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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.
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ICASSP 2023 声学回声消除挑战赛
ICASSP 2023 声学回声消除挑战赛旨在激励声学回声消除(AEC)方面的研究,这是语音增强的一个重要领域,目前仍是音频通信领域的首要问题。这是第四届声学回声消除挑战赛,通过增加个性化声学回声消除的第二个赛道、将算法+缓冲延迟降低到 20 毫秒以及包括全频段版本的 AECMOS(Purin 等人,2020 年),该挑战赛得到了加强。我们开源了两个大型数据集,用于在单人通话和双人通话场景下训练 AEC 模型。这些数据集包括来自 10,000 多个真实音频设备和真实环境中人类扬声器的录音,以及一个合成数据集。我们开源了一个在线主观测试框架,并为研究人员快速测试其结果提供了一个客观指标。本次挑战赛的优胜者是根据在所有场景中取得的平均意见分(MOS)和单词准确率(WAcc)选出的。
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5.30
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审稿时长
22 weeks
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