Interspeech 2021 Deep Noise Suppression Challenge
| Autor: | Reddy, Chandan K A, Dubey, Harishchandra, Koishida, Kazuhito, Nair, Arun, Gopal, Vishak, Cutler, Ross, Braun, Sebastian, Gamper, Hannes, Aichner, Robert, Srinivasan, Sriram |
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| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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 and ICASSP 2020. We open-sourced training and test datasets for the wideband scenario. We also open-sourced a subjective evaluation framework based on ITU-T standard P.808, which was also used to evaluate participants of the challenge. Many researchers from academia and industry made significant contributions to push the field forward, yet even the best noise suppressor was far from achieving superior speech quality in challenging scenarios. In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios. The two tracks in this challenge will focus on real-time denoising for (i) wide band, and(ii) full band scenarios. We are also making available a reliable non-intrusive objective speech quality metric called DNSMOS for the participants to use during their development phase. Comment: arXiv admin note: substantial text overlap with arXiv:2009.06122 |
| Databáze: | arXiv |
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