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### DNS Challenge's dataset
The Deep Noise Suppression (DNS) Challenge is a single-channel speech enhancement
challenge organized by Microsoft, with a focus on real-time applications.
More info can be found on the [official page](https://dns-challenge.azurewebsites.net/).
**References**
The challenge paper, [here](https://arxiv.org/abs/2001.08662).
```BibTex
@misc{DNSChallenge2020,
title={The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework},
author={Chandan K. A. Reddy and Ebrahim Beyrami and Harishchandra Dubey and Vishak Gopal and Roger Cheng and Ross Cutler and Sergiy Matusevych and Robert Aichner and Ashkan Aazami and Sebastian Braun and Puneet Rana and Sriram Srinivasan and Johannes Gehrke}, year={2020},
doi=https://doi.org/10.48550/arXiv.2001.08662,
}
```

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### Microsoft Scalable Noisy Speech Dataset (MS-SNSD)
MS-SNSD is a speech datasetthat can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired.
### Dataset download & setup
- Follow steps in the official repo [here](https://github.com/microsoft/MS-SNSD) to download and setup the dataset.
**References**
```BibTex
@article{reddy2019scalable,
title={A Scalable Noisy Speech Dataset and Online Subjective Test Framework},
author={Reddy, Chandan KA and Beyrami, Ebrahim and Pool, Jamie and Cutler, Ross and Srinivasan, Sriram and Gehrke, Johannes},
journal={Proc. Interspeech 2019},
pages={1816--1820},
year={2019}
}
```

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_target_: mayavoz.data.dataset.MayaDataset
root_dir : /Users/shahules/Myprojects/MS-SNSD
name : dns-2020
name : MS-SNSD
duration : 2.0
sampling_rate: 16000
batch_size: 32

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_target_: mayavoz.data.dataset.MayaDataset
root_dir : /Users/shahules/Myprojects/MS-SNSD
name : dns-2020
name : MS-SNSD
duration : 2.0
sampling_rate: 16000
batch_size: 32

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**References**
```BibTex
@misc{DNSChallenge2020,
@misc{
title={Noisy speech database for training speech enhancement algorithms and TTS models},
author={Valentini-Botinhao, Cassia}, year={2017},
doi=https://doi.org/10.7488/ds/2117,