---
title: NSynth
canonical_url: "https://www.modelscope.cn/datasets/OmniData/NSynth"
md_url: "https://www.modelscope.cn/datasets/OmniData/NSynth.md"
repository: OmniData/NSynth
last_updated: 2024-07-14
license: "[CC BY 4.0]"
storage_size: "24 GB"
domain:
  - publishDate
  - taskTypes
  - publisher
  - paperUrl
  - publishUrl
  - displayName
  - mediaTypes
tasks:
  - 2017-04-05
  - "Audio Generation"
  - Google
  - "https://arxiv.org/pdf/1704.01279v1.pdf"
  - "https://magenta.tensorflow.org/datasets/nsynth"
  - NSynth
  - Video
downloads: 13128
stars: 0
---

# NSynth

> NSynth - OmniData 在 ModelScope 开源的数据集。displayName: NSynth license: CC BY 4.0 mediaTypes: Video paperUrl: https://arxiv.org/pdf/1704.01279v1.pdf publishDate: "2017-04-05" publishUrl: https://magenta.tensorflow.org/datasets/nsynth publisher: DeepMind Google…

OmniData/NSynth 是 ModelScope 魔搭社区上的2017-04-05、Audio Generation、Google数据集，涉及 publishDate、taskTypes、publisher 领域，存储大小 24 GB，采用 [CC BY 4.0] 许可。

- **Repository**: OmniData/NSynth
- **License**: [CC BY 4.0]
- **Tasks**: 2017-04-05, Audio Generation, Google, https://arxiv.org/pdf/1704.01279v1.pdf, https://magenta.tensorflow.org/datasets/nsynth, NSynth, Video
- **Domain**: publishDate, taskTypes, publisher, paperUrl, publishUrl, displayName, mediaTypes
- **Storage size**: 24 GB
- **Downloads**: 13128
- **Stars**: 0
- **Last updated**: 2024-07-14

Source: https://www.modelscope.cn/datasets/OmniData/NSynth

---

displayName: NSynth
license:
- CC BY 4.0
mediaTypes:
- Video
paperUrl: https://arxiv.org/pdf/1704.01279v1.pdf
publishDate: "2017-04-05"
publishUrl: https://magenta.tensorflow.org/datasets/nsynth
publisher:
- DeepMind
- Google
tags:
- Flute
- Guitar
- Keyboard
taskTypes:
- Self Supervised Learning
- Music Generation
- Audio Generation

---
# 数据集介绍
  ## 简介
   NSynth 是一个包含 305,979 个音符的音频数据集，每个音符都有独特的音高、音色和包络。对于来自商业样本库的 1,006 种乐器，我们生成了 4 秒的单声道 16kHz 音频片段，称为音符，通过覆盖标准 MIDI 钢琴 o (21-108) 的每个音高以及五种不同的力度（25、50、 75、100、127）。音符在前三秒保持不变，并在最后一秒衰减。 
  ## 引文
  
```
"@inproceedings{engel2017neural,
title={Neural audio synthesis of musical notes with wavenet autoencoders},
author={Engel, Jesse and Resnick, Cinjon and Roberts, Adam and Dieleman, Sander and Norouzi, Mohammad and Eck, Douglas and Simonyan, Karen},
booktitle={International Conference on Machine Learning},
pages={1068--1077},
year={2017},
organization={PMLR}
}"
```

  
## Download dataset
:modelscope-code[]{type="git"}