Interspeech 2021 Deep Noise Suppression Challenge
By Chandan K. A. Reddy, Harishchandra Dubey, Kazuhito Koishida, Arun Asokan Nair, Vishak Gopal, Ross Cutler, Sebastian Braun, Hannes Gamper, Robert Aichner, Sriram Srinivasan. 2021
206 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Biomedical Engineering, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ross Cutler
- Babak Naderi
- Vishak Gopal
- Sebastian Braun
- Chandan K. A. Reddy
- Takuya Yoshioka
- Robert Aichner
- Harishchandra Dubey
In Biomedical Engineering
- Ross Cutler
- Vishak Gopal
- Chandan K. A. Reddy
- Sebastian Braun
- Harishchandra Dubey
- Robert Aichner
- Sriram Srinivasan
- Hannes Gamper
In Signal Processing
- Ross Cutler
- Vishak Gopal
- Chandan K. A. Reddy
- Babak Naderi
- Robert Aichner
- Hannes Gamper
- Sebastian Braun
- Harishchandra Dubey
Citations in the corpus
- Icassp 2022 Deep Noise Suppression Challenge (2022, methodology): “We trained NSNet2 [14, 15] on fullband non-personalized training dataset to obtain the baseline.”
- Personalized speech enhancement: new models and Comprehensive evaluation (2021, methodology): “We used the clean speech data of the deep noise suppression (DNS) challange [23].”
- Subjective Evaluation of Noise Suppression Algorithms in Crowdsourcing (2021, methodology): “Our P.835 tool was used in the the INTERSPEECH 2021 Deep Noise Suppression Challenge [1] as the primary evaluation metric.”
- ICASSP 2023 Deep Noise Suppression Challenge (2023, background): “(vi) Track-1 and Track-2 has their separate subjective evaluations to ensure we have headset testclips evaluated together, and speakerphone testclips evaluated together.”
- FullSubNet+: Channel Attention Fullsubnet with Complex Spectrograms for Speech Enhancement (2022, methodology): “As a result, FullSubNet achieves excellent results on DNS Challenge dataset [14].”