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.
The mean opinion score (MOS) is standardized for the perceptual evaluation of speech quality and is obtained by asking listeners to rate the quality of a speech sample.
This is the third AEC challenge and it is enhanced by including mobile scenarios, adding speech recognition rate in the challenge goal metrics, and making the default sample rate 48 kHz.
1 code implementation • 27 Feb 2022 • Harishchandra Dubey, Vishak Gopal, Ross Cutler, Ashkan Aazami, Sergiy Matusevych, Sebastian Braun, Sefik Emre Eskimez, Manthan Thakker, Takuya Yoshioka, Hannes Gamper, Robert Aichner
We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P. 835 to rate and rank-order the challenge entries.
Deep learning based speech enhancement has made rapid development towards improving quality, while models are becoming more compact and usable for real-time on-the-edge inference.
It is shown that the achievable speech quality is a function of network complexity, and show which models have better tradeoffs.
2 code implementations • 6 Jan 2021 • Chandan K A Reddy, Harishchandra Dubey, Kazuhito Koishida, Arun Nair, Vishak Gopal, Ross Cutler, Sebastian Braun, Hannes Gamper, Robert Aichner, Sriram Srinivasan
In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios.
In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios.
Acoustic echo cancellation Audio and Speech Processing Sound
Diffracted scattering and occlusion are important acoustic effects in interactive auralization and noise control applications, typically requiring expensive numerical simulation.