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arxiv 2309.12553 v1 pith:ELCYVGBN submitted 2023-09-22 eess.AS cs.SD

ICASSP 2023 Acoustic Echo Cancellation Challenge

classification eess.AS cs.SD
keywords acousticcancellationchallengeechoaudiodatasetsicasspopen
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 20ms, as well as including a full-band version of AECMOS. 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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. LMPAN: A Lightweight Multi-Path Alignment Network for Joint Full-Duplex Acoustic Echo Cancellation and Noise Suppression

    eess.AS 2026-07 unverdicted novelty 5.0

    LMPAN is a 480K-parameter network using multi-path alignment, attention integration, and dynamic post-filtering that matches larger models on joint AEC and NS while supporting real-time inference.