# Interspeech 2021 Deep Noise Suppression Challenge

arXiv: https://arxiv.org/abs/2101.01902 · HTML: https://mlsanity.com/paper/2101.01902/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [A scalable noisy speech dataset and online subjective test framework](https://mlsanity.com/paper/1909.08050/) (2019)
- [THCHS-30 : A Free Chinese Speech Corpus](https://mlsanity.com/paper/1512.01882/) (2015)
- [An Open source Implementation of ITU-T Recommendation P.808 with Validation](https://mlsanity.com/paper/2005.08138/) (2020)
- [ICASSP 2021 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2009.06122/) (2020)
- [Raw Waveform-based Speech Enhancement by Fully Convolutional Networks](https://mlsanity.com/paper/1703.02205/) (2017)
- [Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks](https://mlsanity.com/paper/2005.11611/) (2020)
- [Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net](https://mlsanity.com/paper/2006.00687/) (2020)
- [An individualized super Gaussian single microphone Speech Enhancement for hearing aid users with smartphone as an assistive device](https://mlsanity.com/paper/1812.03916/) (2018)
- [Subjective Evaluation of Noise Suppression Algorithms in Crowdsourcing](https://mlsanity.com/paper/2010.13200/) (2020)

## Built on by (most cited first, 30 of 47)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration](https://mlsanity.com/paper/2301.12686/) (2023)
- [Towards Robust Speech Representation Learning for Thousands of Languages](https://mlsanity.com/paper/2407.00837/) (2024)
- [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021)
- [FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement](https://mlsanity.com/paper/2203.12188/) (2022)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (2022)
- [Personalized Speech Enhancement: New Models and Comprehensive Evaluation](https://mlsanity.com/paper/2110.09625/) (2021)
- [Audio Super-Resolution with Latent Bridge Models](https://mlsanity.com/paper/2509.17609/) (2025)
- [Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation](https://mlsanity.com/paper/2204.00771/) (2022)
- [Music Enhancement via Image Translation and Vocoding](https://mlsanity.com/paper/2204.13289/) (2022)
- [ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding](https://mlsanity.com/paper/2207.09514/) (2022)
- [Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization](https://mlsanity.com/paper/2210.07692/) (2022)
- [Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning](https://mlsanity.com/paper/2308.09514/) (2023)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data](https://mlsanity.com/paper/2203.12369/) (2022)
- [ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression](https://mlsanity.com/paper/2305.16665/) (2023)
- [VoiceBridge: General Speech Restoration with One-step Latent Bridge Models](https://mlsanity.com/paper/2509.25275/) (2025)
- [Towards Real-Time Generative Speech Restoration with Flow-Matching](https://mlsanity.com/paper/2510.16997/) (2025)
- [A Mask Free Neural Network for Monaural Speech Enhancement](https://mlsanity.com/paper/2306.04286/) (2023)
- [Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity](https://mlsanity.com/paper/2502.01330/) (2025)
- [A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect](https://mlsanity.com/paper/2105.03409/) (2021)
- [Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2307.12134/) (2023)
- [Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression](https://mlsanity.com/paper/2308.11053/) (2023)
- [Spatial HuBERT: Self-supervised Spatial Speech Representation Learning for a Single Talker from Multi-channel Audio](https://mlsanity.com/paper/2310.10922/) (2023)
- [Towards Sub-millisecond Latency Real-Time Speech Enhancement Models on Hearables](https://mlsanity.com/paper/2409.18239/) (2024)
- [CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR](https://mlsanity.com/paper/2502.20040/) (2025)
- [A Semantic Information-based Hierarchical Speech Enhancement Method Using Factorized Codec and Diffusion Model](https://mlsanity.com/paper/2505.13843/) (2025)
- [Rethinking Flow and Diffusion Bridge Models for Speech Enhancement](https://mlsanity.com/paper/2602.18355/) (2026)
- [A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks](https://mlsanity.com/paper/2112.04939/) (2021)

## Abstract (arXiv)

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.