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Datasets:
Fhrozen
/
AudioSet2K22

Tasks:
Audio Classification
Size Categories: 100K<n<100M
Language Creators: unknown
Annotations Creators: unknown
Source Datasets: unknown
Tags: audio-slot-filling
License: cc-by-sa-4.0
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    AttributeError
Message:      'NoneType' object has no attribute 'builder_configs'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 361, in get_dataset_config_names
                  return list(builder_cls.builder_configs.keys()) or [dataset_module.builder_kwargs.get("config_name", "default")]
              AttributeError: 'NoneType' object has no attribute 'builder_configs'

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Dataset Card for audioset2022

Dataset Summary

The AudioSet ontology is a collection of sound events organized in a hierarchy. The ontology covers a wide range of everyday sounds, from human and animal sounds, to natural and environmental sounds, to musical and miscellaneous sounds.

This repository only includes audio files for DCASE 2022 - Task 3

The included labels are limited to:

  • Female speech, woman speaking
  • Male speech, man speaking
  • Clapping
  • Telephone
  • Telephone bell ringing
  • Ringtone
  • Laughter
  • Domestic sounds, home sounds
  • Vacuum cleaner
  • Kettle whistle
  • Mechanical fan
  • Walk, footsteps
  • Door
  • Cupboard open or close
  • Music
  • Background music
  • Pop music
  • Musical instrument
  • Acoustic guitar
  • Marimba, xylophone
  • Cowbell
  • Piano
  • Electric piano
  • Rattle (instrument)
  • Water tap, faucet
  • Bell
  • Bicycle bell
  • Chime
  • Knock

Supported Tasks and Leaderboards

  • audio-classification: The dataset can be used to train a model for Sound Event Detection/Localization.

The recordings only includes the single channel audio. For Localization tasks, it will required to apply RIR information

Languages

None

Dataset Structure

Data Instances

WIP

{
    'file': 

}

Data Fields

  • file: A path to the downloaded audio file in .mp3 format.

Data Splits

This dataset only includes audio file from the unbalance train list. The data comprises two splits: weak labels and strong labels.

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

The dataset was initially downloaded by Nelson Yalta ([email protected]).

Licensing Information

CC BY-SA 4.0

Citation Information 

@inproceedings{45857,
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}
}
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Homepage:
AudioSet Ontology
Paper:
Audio Set: An ontology and human-labeled dataset for audio events
Leaderboard:
Paperswithcode Leaderboard

Models trained or fine-tuned on Fhrozen/AudioSet2K22