Crowdsourcing approach for subjective evaluation of echo impairment
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Abstract (arXiv)
The quality of acoustic echo cancellers (AECs) in real-time communication systems is typically evaluated using objective metrics like ERLE and PESQ, and less commonly with lab-based subjective tests like ITU-T Rec. P.831. We will show that these objective measures are not well correlated to subjective measures. We then introduce an open-source crowdsourcing approach for subjective evaluation of echo impairment which can be used to evaluate the performance of AECs. We provide a study that shows this tool is accurate and highly reproducible. This new tool has been recently used in the ICASSP 2021 AEC Challenge which made the challenge possible to do quickly and cost effectively.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2021End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppressioncited by 6
- 2022Deep model with built-in cross-attention alignment for acoustic echo cancellationcited by 2
- 2026Knowledge Distillation for Efficient Acoustic Echo Controlcited by 0
- 2026Knowledge Distillation for Efficient Acoustic Echo Control26 Aug 2026
- 2022Deep model with built-in cross-attention alignment for acoustic echo cancellation24 Aug 2022
- 2021End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression2 Oct 2021