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ESC-50: Dataset for Environmental Sound Classification

Overview | Download | Results | Repository content | License | Citing | Caveats | Changelog

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The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification.

The dataset consists of 5-second-long recordings organized into 50 semantical classes (with 40 examples per class) loosely arranged into 5 major categories:

Clips in this dataset have been manually extracted from public field recordings gathered by the Freesound.org project. The dataset has been prearranged into 5 folds for comparable cross-validation, making sure that fragments from the same original source file are contained in a single fold.

A more thorough description of the dataset is available in the original paper with some supplementary materials on GitHub: ESC: Dataset for Environmental Sound Classification - paper replication data.

Download

The dataset can be downloaded as a single .zip file (~600 MB):

Download ESC-50 dataset

Results

Supervised Methods

Numerous machine learning & signal processing approaches have been evaluated on the ESC-50 dataset. Most of them are listed here. If you know of some other reference, you can message me or open a Pull Request directly.

Terms used in the table:

_{• CNN - Convolutional Neural Network<br />• CRNN - Convolutional Recurrent Neural Network<br />• GMM - Gaussian Mixture Model<br />• GTCC - Gammatone Cepstral Coefficients<br />• GTSC - Gammatone Spectral Coefficients<br />• k-NN - k-Neareast Neighbors<br />• MFCC - Mel-Frequency Cepstral Coefficients<br />• MLP - Multi-Layer Perceptron<br />• RBM - Restricted Boltzmann Machine<br />• RNN - Recurrent Neural Network<br />• SVM - Support Vector Machine<br />• TEO - Teager Energy Operator<br />• ZCR - Zero-Crossing Rate}

Unsupervised Methods

ESC-50 was also evaluated in unsupervised learning settings (Zhao et al., 2022):

1. Zero shot (ZS): Human supervision (i.e., labeled audio-text pairs outside the ESC-50 domain) may be used in training.
2. Zero resource (ZR): No human supervision (i.e., any form of manually labeled audio-text pairs) is used in training.

Repository content

• audio/*.wav

  2000 audio recordings in WAV format (5 seconds, 44.1 kHz, mono) with the following naming convention:

  {FOLD}-{CLIP_ID}-{TAKE}-{TARGET}.wav

  • {FOLD} - index of the cross-validation fold,
  • {CLIP_ID} - ID of the original Freesound clip,
  • {TAKE} - letter disambiguating between different fragments from the same Freesound clip,
  • {TARGET} - class in numeric format [0, 49].

• meta/esc50.csv

  CSV file with the following structure:

  <sub>filename</sub>	<sub>fold</sub>	<sub>target</sub>	<sub>category</sub>	<sub>esc10</sub>	<sub>src_file</sub>	<sub>take</sub>

  The esc10 column indicates if a given file belongs to the ESC-10 subset (10 selected classes, CC BY license).

• meta/esc50-human.xlsx

  Additional data pertaining to the crowdsourcing experiment (human classification accuracy).

License

The dataset is available under the terms of the Creative Commons Attribution Non-Commercial license.

A smaller subset (clips tagged as ESC-10) is distributed under CC BY (Attribution).

Attributions for each clip are available in the LICENSE file.

Citing

<a href="http://karol.piczak.com/papers/Piczak2015-ESC-Dataset.pdf"><img src="https://img.shields.io/badge/download%20paper-PDF-ff69b4.svg" alt="Download paper in PDF format" title="Download paper in PDF format" align="right" /></a>

If you find this dataset useful in an academic setting please cite:

K. J. Piczak. ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia, 2015.

[DOI: http://dx.doi.org/10.1145/2733373.2806390]

@inproceedings{piczak2015dataset,
  title = {{ESC}: {Dataset} for {Environmental Sound Classification}},
  author = {Piczak, Karol J.},
  booktitle = {Proceedings of the 23rd {Annual ACM Conference} on {Multimedia}},
  date = {2015-10-13},
  url = {http://dl.acm.org/citation.cfm?doid=2733373.2806390},
  doi = {10.1145/2733373.2806390},
  location = {{Brisbane, Australia}},
  isbn = {978-1-4503-3459-4},
  publisher = {{ACM Press}},
  pages = {1015--1018}
}

Caveats

Please be aware of potential information leakage while training models on ESC-50, as some of the original Freesound recordings were already preprocessed in a manner that might be class dependent (mostly bandlimiting). Unfortunately, this issue went unnoticed when creating the original version of the dataset. Due to the number of methods already evaluated on ESC-50, no changes rectifying this issue will be made in order to preserve comparability.

Changelog

v2.0.0 (2017-12-13)

_{• Change to WAV version as default.}

v2.0.0-pre (2016-10-10) (wav-files branch)

_{• Replace OGG recordings with cropped WAV files for easier loading and frame-level precision (some of the OGG recordings had a slightly different length when loaded).<br/>• Move recordings to a one directory structure with a meta CSV file.}

v1.0.0 (2015-04-15)

_{• Initial version of the dataset (OGG format).}