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 CAIMAN-ASR models.
Dataset Details
Dataset Description
Curated by: Myrtle.ai
License: Myrtle.ai's modifications to the source data are licensed under the CC BY 4.0 license. Some of the original data is under the CC 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:
>>> 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, as collected for the MUSAN dataset. All these audios are in the public domain.
- The remaining 312 audios were collected from YouTube videos marked as CC BY 3.0. Specific attributions are here
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.