  The INTERSPEECH 2020 Deep Noise Suppression (DNS) Challenge is intended to
promote collaborative research in real-time single-channel Speech Enhancement
aimed to maximize the subjective (perceptual) quality of the enhanced speech. A
typical approach to evaluate the noise suppression methods is to use objective
metrics on the test set obtained by splitting the original dataset. While the
performance is good on the synthetic test set, often the model performance
degrades significantly on real recordings. Also, most of the conventional
objective metrics do not correlate well with subjective tests and lab
subjective tests are not scalable for a large test set. In this challenge, we
open-sourced a large clean speech and noise corpus for training the noise
suppression models and a representative test set to real-world scenarios
consisting of both synthetic and real recordings. We also open-sourced an
online subjective test framework based on ITU-T P.808 for researchers to
reliably test their developments. We evaluated the results using P.808 on a
blind test set. The results and the key learnings from the challenge are
discussed. The datasets and scripts can be found here for quick access
https://github.com/microsoft/DNS-Challenge.
