The Deep Noise Suppression (DNS) challenge is designed to foster innovation
in the area of noise suppression to achieve superior perceptual speech quality.
We recently organized a DNS challenge special session at INTERSPEECH 2020. We
open sourced training and test datasets for researchers to train their noise
suppression models. We also open sourced a subjective evaluation framework and
used the tool to evaluate and pick the final winners. Many researchers from
academia and industry made significant contributions to push the field forward.
We also learned that as a research community, we still have a long way to go in
achieving excellent speech quality in challenging noisy real-time conditions.
In this challenge, we are expanding both our training and test datasets. There
are two tracks with one focusing on real-time denoising and the other focusing
on real-time personalized deep noise suppression. We also make a non-intrusive
objective speech quality metric called DNSMOS available for participants to use
during their development stages. The final evaluation will be based on
subjective tests.

本研究旨在促进噪声抑制技术的创新，提高感知语音质量。通过在 INTERSPEECH 2020 会议上举行 Deep Noise Suppression Challenge 特别会议以及开源训练和测试数据集，来推动该领域的研究。挑战赛分为两个方向，一个专注于实时去噪，另一个专注于实时个性化深度噪声抑制。我们还提供了一种非侵入式的客观语音质量指标 DNSMOS，在开发阶段供参与者使用，最终评估将基于主观测试。