ICASSP 2021 Deep Noise Suppression Challenge

• Main Authors: Reddy, Chandan K. A., Dubey, Harishchandra, Gopal, Vishak, Cutler, Ross, Braun, Sebastian, Gamper, Hannes, Aichner, Robert, Srinivasan, Sriram
• Format: Conference Proceeding
• Language: English
• Subjects: Conferences; Deep Noise Suppressor; DNS; Industries; Machine Learning; Noise reduction; P.808; Perceptual Speech Quality; Speech enhancement; Technological innovation; Training
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9415105

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 where 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 select 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 expanded both our training and test datasets. Clean speech in the training set has increased by 200% with the addition of singing voice, emotion data, and non-English languages. The test set has increased by 100% with the addition of singing, emotional, non-English (tonal and non-tonal) languages, and, personalized DNS test clips. There are two tracks with focus on (i) real-time denoising, and (ii) real-time personalized DNS. We present the challenge results at the end.