Interspeech 2021 Deep Noise Suppression Challenge

• URL: http://arxiv.org/abs/2101.01902v3
• Date: Mon, 5 Apr 2021 01:19:31 GMT
• Title: Interspeech 2021 Deep Noise Suppression Challenge
• Authors: Chandan K A Reddy, Harishchandra Dubey, Kazuhito Koishida, Arun Nair, Vishak Gopal, Ross Cutler, Sebastian Braun, Hannes Gamper, Robert Aichner, Sriram Srinivasan
• Abstract summary: DNS challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. We open-sourced training and test datasets for the wideband scenario. In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios.
• Score: 41.68545171728067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• The VoxCeleb Speaker Recognition Challenge: A Retrospective [75.40776645175585]
  The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of speaker recognition and diarisation under various settings. We provide a review of these challenges that covers: what they explored; the methods developed by the challenge participants and how these evolved.
  arXiv  Detail & Related papers  (2024-08-27T08:57:31Z)
• DiffSED: Sound Event Detection with Denoising Diffusion [70.18051526555512]
  We reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process. During training, our model learns to reverse the noising process by converting noisy latent queries to the groundtruth versions.
  arXiv  Detail & Related papers  (2023-08-14T17:29:41Z)
• The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation [97.63185634482552]
  We summarize the winning solutions from the RoboDepth Challenge. The challenge was designed to facilitate and advance robust OoD depth estimation. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation.
  arXiv  Detail & Related papers  (2023-07-27T17:59:56Z)
• DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation [12.734839065028547]
  This paper proposes a real-time cross-attention deep model named DeepVQE, based on residual convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We conduct ablation studies analyze the contributions of different components of our model to achieve the overall performance. DeepVQE state-of-the-art performance on nonpersonalized tracks from the ICASSP 2023 Acoustic Echo Challenge and ICASSP 2023 Deep Noise Suppression Challenge test sets, showing that a single model can handle multiple tasks with excellent performance.
  arXiv  Detail & Related papers  (2023-06-05T18:37:05Z)
• ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection [70.45884214674057]
  ASVspoof 2021 is the forth edition in the series of bi-annual challenges which aim to promote the study of spoofing. This paper describes all three tasks, the new databases for each of them, the evaluation metrics, four challenge baselines, the evaluation platform and a summary of challenge results.
  arXiv  Detail & Related papers  (2021-09-01T16:17:31Z)
• Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions [37.68195595947483]
  This task focuses on the inevitable problem for the practical use of ASD systems. The main challenge of this task is to detect unknown anomalous sounds where the acoustic characteristics of the training and testing samples are different.
  arXiv  Detail & Related papers  (2021-06-08T16:26:10Z)
• Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds [55.18259748448095]
  This report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process. The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features.
  arXiv  Detail & Related papers  (2020-06-18T10:49:49Z)
• The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results [27.074806625047646]
  DNS Challenge is intended to promote collaborative research in real-time single-channel Speech Enhancement. We open-sourced a large clean speech and noise corpus for training the noise suppression models. We also open-sourced an online subjective test framework based on ITU-T P.808 for researchers to reliably test their developments.
  arXiv  Detail & Related papers  (2020-05-16T23:48:37Z)
• The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework [27.074806625047646]
  The INTERSPEECH 2020 Deep Noise Suppression Challenge is intended to promote collaborative research in real-time single-channel Speech Enhancement. We open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios. The winners of this challenge will be selected based on subjective evaluation on a representative test set using P.808 framework.
  arXiv  Detail & Related papers  (2020-01-23T17:00:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.