  The Deep Noise Suppression (DNS) challenge is designed to foster innovation
in the area of noise suppression to achieve superior perceptual speech quality.
This is the 4th DNS challenge, with the previous editions held at INTERSPEECH
2020, ICASSP 2021, and INTERSPEECH 2021. 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. We provide access to DNSMOS P.835 and word accuracy (WAcc)
APIs to challenge participants to help with iterative model improvements. In
this challenge, we introduced the following changes: (i) Included mobile device
scenarios in the blind test set; (ii) Included a personalized noise suppression
track with baseline; (iii) Added WAcc as an objective metric; (iv) Included
DNSMOS P.835; (v) Made the training datasets and test sets fullband (48 kHz).
We use an average of WAcc and subjective scores P.835 SIG, BAK, and OVRL to get
the final score for ranking the DNS models. We believe that as a research
community, we still have a long way to go in achieving excellent speech quality
in challenging noisy real-world scenarios.
