PESQ

Physics

Teleconference

Metric (unit)

Test (biology)

Open source

Computer network

Multimedia

Biology

Reverberation

Noise (video)

Background noise

Image (mathematics)

Epistemology

Engineering

Philosophy

Paleontology

Artificial intelligence

Acoustics

Programming language

Operations management

Speech enhancement

Quality (philosophy)

Software

Echo (communications protocol)

Computer science

Mean opinion score

Speech recognition

Telecommunications

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.

ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets, Testing Framework, and Results