ARCA23K (original) (raw)
Published September 15, 2021 | Version 1.0
Dataset Open
Authors/Creators
- 1. University of Surrey
Description
ARCA23K is a dataset of labelled sound events created to investigate real-world label noise. It contains 23,727 audio clips originating from Freesound, and each clip belongs to one of 70 classes taken from the AudioSet ontology. The dataset was created using an entirely automated process with no manual verification of the data. For this reason, many clips are expected to be labelled incorrectly.
In addition to ARCA23K, this release includes a companion dataset called ARCA23K-FSD, which is a single-label subset of the FSD50K dataset. ARCA23K-FSD contains the same sound classes as ARCA23K and the same number of audio clips per class. As it is a subset of FSD50K, each clip and its label have been manually verified. Note that only the ground truth data of ARCA23K-FSD is distributed in this release. To download the audio clips, please visit the Zenodo page for FSD50K.
A paper has been published detailing how the dataset was constructed. See the Citing section below.
The source code used to create the datasets is available: https://github.com/tqbl/arca23k-dataset
Characteristics
- ARCA23K(-FSD) is divided into:
- A training set containing 17,979 clips (39.6 hours for ARCA23K).
- A validation set containing 2,264 clips (5.0 hours).
- A test test containing 3,484 clips (7.3 hours).
- There are 70 sound classes in total. Each class belongs to the AudioSet ontology.
- Each audio clip was sourced from the Freesound database. Other than format conversions (e.g. resampling), the audio clips have not been modified.
- The duration of the audio clips varies from 0.3 seconds to 30 seconds.
- All audio clips are mono 16-bit WAV files sampled at 44.1 kHz.
- Based on listening tests (details in paper), 46.4% of the training examples are estimated to be labelled incorrectly. Among the incorrectly-labelled examples, 75.9% are estimated to be out-of-vocabulary.
Sound Classes
The list of sound classes is given below. They are grouped based on the top-level superclasses of the AudioSet ontology.
Music
- Acoustic guitar
- Bass guitar
- Bowed string instrument
- Crash cymbal
- Electric guitar
- Gong
- Harp
- Organ
- Piano
- Rattle (instrument)
- Scratching (performance technique)
- Snare drum
- Trumpet
- Wind chime
- Wind instrument, woodwind instrument
Sounds of things
- Boom
- Camera
- Coin (dropping)
- Computer keyboard
- Crack
- Dishes, pots, and pans
- Drawer open or close
- Drill
- Gunshot, gunfire
- Hammer
- Keys jangling
- Knock
- Microwave oven
- Printer
- Sawing
- Scissors
- Skateboard
- Slam
- Splash, splatter
- Squeak
- Tap
- Thump, thud
- Toilet flush
- Train
- Water tap, faucet
- Whoosh, swoosh, swish
- Writing
- Zipper (clothing)
Natural sounds
- Crackle
- Stream
- Waves, surf
- Wind
Human sounds
- Burping, eructation
- Chewing, mastication
- Child speech, kid speaking
- Clapping
- Cough
- Crying, sobbing
- Fart
- Female singing
- Female speech, woman speaking
- Finger snapping
- Giggle
- Male speech, man speaking
- Run
- Screaming
- Walk, footsteps
Animal
- Bark
- Cricket
- Livestock, farm animals, working animals
- Meow
- Rattle
Source-ambiguous sounds
- Crumpling, crinkling
- Crushing
- Tearing
License and Attribution
This release is licensed under the Creative Commons Attribution 4.0 International License.
The audio clips distributed as part of ARCA23K were sourced from Freesound and have their own Creative Commons license. The license information and attribution for each audio clip can be found in ARCA23K.metadata/train.json, which also includes the original Freesound URLs.
The files under ARCA23K-FSD.ground_truth/ are an adaptation of the ground truth data provided as part of FSD50K, which is licensed under the Creative Commons Attribution 4.0 International License. The curators of FSD50K are Eduardo Fonseca, Xavier Favory, Jordi Pons, Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano, and Sara Fernandez.
Citing
If you wish to cite this work, please cite the following paper:
T. Iqbal, Y. Cao, A. Bailey, M. D. Plumbley, and W. Wang, “ARCA23K: An audio dataset for investigating open-set label noise”, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 2021, Barcelona, Spain, pp. 201–205.
BibTeX:
@inproceedings{Iqbal2021, author = {Iqbal, T. and Cao, Y. and Bailey, A. and Plumbley, M. D. and Wang, W.}, title = {{ARCA23K}: An audio dataset for investigating open-set label noise}, booktitle = {Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021)}, pages = {201--205}, year = {2021}, address = {Barcelona, Spain}, }
Files
ARCA23K-FSD.ground_truth.zip
Files (8.9 GB)