README.md

metadata

language:
  - en
license: cc-by-4.0
size_categories:
  - 10K<n<100K
  - 1M<n<10M
source_datasets:
  - original
task_categories:
  - audio-classification
paperswithcode_id: audioset
pretty_name: AudioSet
config_names:
  - balanced
  - unbalanced
tags:
  - audio
configs:
  - config_name: balanced
    default: true
    data_files:
      - split: train
        path: data/bal_train/*.parquet
      - split: test
        path: data/eval/*.parquet
  - config_name: unbalanced
    data_files:
      - split: train
        path: data/unbal_train/*.parquet
      - split: test
        path: data/eval/*.parquet
  - config_name: full
    data_files:
      - split: bal_train
        path: data/bal_train/*.parquet
      - split: unbal_train
        path: data/unbal_train/*.parquet
      - split: eval
        path: data/eval/*.parquet

Dataset Card for AudioSet

Dataset Description

• Homepage: https://research.google.com/audioset/index.html
• Paper: https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/45857.pdf
• Leaderboard: https://paperswithcode.com/sota/audio-classification-on-audioset

Dataset Summary

AudioSet is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology.

Supported Tasks and Leaderboards

• audio-classification: Classify audio clips into categories. The leaderboard is available here

Languages

The class labels in the dataset are in English.

Dataset Structure

Data Instances

Example instance from the dataset:

{
 'video_id': '--PJHxphWEs',
 'audio': {
  'path': 'audio/bal_train/--PJHxphWEs.flac',
  'array': array([-0.04364824, -0.05268681, -0.0568949 , ...,  0.11446512,
          0.14912748,  0.13409865]),
  'sampling_rate': 48000
 },
 'labels': ['/m/09x0r', '/t/dd00088'],
 'human_labels': ['Speech', 'Gush']
}

Data Fields

Instances have the following fields:

• video_id: a string feature containing the original YouTube ID.
• audio: an Audio feature containing the audio data and sample rate.
• labels: a sequence of string features containing the labels associated with the audio clip.
• human_labels: a sequence of string features containing the human-readable forms of the same labels as in labels.

Data Splits

The distribuion of audio clips is as follows:

balanced configuration

	train	test
# instances	18683	17141

unbalanced configuration

	train	test
# instances	1738657	17141

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

The labels are from the AudioSet ontology. Audio clips are from YouTube.

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

1. The YouTube videos in this copy of AudioSet were downloaded in March 2023, so not all of the original audios are available. The number of clips able to be downloaded is as follows:
   • Balanced train: 18683 audio clips out of 22160 originally.
   • Unbalanced train: 1738788 clips out of 2041789 originally.
   • Evaluation: 17141 audio clips out of 20371 originally.
2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at 44.1 kHz 24 bit. Audio files are stored in the FLAC format.

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The AudioSet data is licensed under CC-BY-4.0

Citation

@inproceedings{jort_audioset_2017,
    title	= {Audio Set: An ontology and human-labeled dataset for audio events},
    author	= {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
    year	= {2017},
    booktitle	= {Proc. IEEE ICASSP 2017},
    address	= {New Orleans, LA}
}