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---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 540419096.23
num_examples: 1155
download_size: 532918294
dataset_size: 540419096.23
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Myrtle/CAIMAN-ASR-BackgroundNoise
This dataset provides background noise audio, suitable for noise augmentation
while training [Myrtle.ai's](https://myrtle.ai/) CAIMAN-ASR models.
## Dataset Details
### Dataset Description
Curated by: [Myrtle.ai](https://myrtle.ai/)
License: Myrtle.ai's modifications to the source data are licensed under
the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
Some of the original data is under the [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/) license; the rest is in the public domain.
Please see the Source Data section below for more information.
## Uses
The noise audio is intended to be combined with speech audio at
signal-to-noise ratios in the range 0--60 dB.
## Dataset Structure
This dataset contains 1155 audios, all in the train split.
You can access the first audio like this:
```python
>>> import datasets
>>> noise = datasets.load_dataset("Myrtle/CAIMAN-ASR-BackgroundNoise")
>>> noise["train"][0]["audio"]["array"]
array([-0.17913818, -0.26080322, -0.1835022 , ..., -0.26644897,
-0.2434082 , -0.25830078])
```
All of the data is 16 kHz and single-channel.
## Dataset Creation
### Source Data
- 843 of the audios originate from
[Free Sound](https://www.freesound.org),
as collected for the [MUSAN](https://www.openslr.org/17/) dataset. All these audios are in the public domain.
- The remaining 312 audios were collected from YouTube videos marked as [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/).
Specific attributions are [here](./youtube_attributions.md)
#### Data Collection and Processing
Any audio with understandable human speech was filtered out.
Random 20s segments of the YouTube audio were selected.
#### Personal and Sensitive Information
Contains no personal information
## Bias, Risks, and Limitations
This dataset contains a large variety of background noises, but not all
types of background noise are included. If your target validation dataset
has a type of background noise not included here, then using this
noise dataset for augmentation may not help.
If your training dataset already contains significant amounts of
background noise, then training with noise augmentation may not be
necessary.
## Dataset Card Contact
hello@myrtle.ai |