Skip to content
ESC-50: Dataset for Environmental Sound Classification
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.circleci
.github Add probot-stale Dec 30, 2017
audio Update dataset (#2) Dec 13, 2017
meta Update dataset (#2) Dec 13, 2017
tests Add tests for mono Jan 9, 2018
.gitignore Update dataset (#2) Dec 13, 2017
LICENSE
README.md Update [kumar2017] reference with source code Apr 6, 2018
esc50.gif Update dataset (#2) Dec 13, 2017
pytest.ini
requirements.txt

README.md

ESC-50: Dataset for Environmental Sound Classification

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

    Download 

ESC-50 clip preview

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:

Animals Natural soundscapes & water sounds Human, non-speech sounds Interior/domestic sounds Exterior/urban noises
Dog Rain Crying baby Door knock Helicopter
Rooster Sea waves Sneezing Mouse click Chainsaw
Pig Crackling fire Clapping Keyboard typing Siren
Cow Crickets Breathing Door, wood creaks Car horn
Frog Chirping birds Coughing Can opening Engine
Cat Water drops Footsteps Washing machine Train
Hen Wind Laughing Vacuum cleaner Church bells
Insects (flying) Pouring water Brushing teeth Clock alarm Airplane
Sheep Toilet flush Snoring Clock tick Fireworks
Crow Thunderstorm Drinking, sipping Glass breaking Hand saw

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

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

Title Notes Accuracy Paper Code
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification CNN with filterbanks learned using convolutional RBM + fusion with GTSC and mel energies 86.50% sailor2017
Learning from Between-class Examples for Deep Sound Recognition EnvNet-v2 (tokozume2017a) + data augmentation + Between-Class learning 84.90% tokozume2017b
Novel Phase Encoded Mel Filterbank Energies for Environmental Sound Classification CNN working with phase encoded mel filterbank energies (PEFBEs), fusion with Mel energies 84.15% tak2017
Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes CNN pretrained on AudioSet 83.50% kumar2017 📜
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification CNN with filterbanks learned using convolutional RBM + fusion with GTSC 83.00% sailor2017
Novel TEO-based Gammatone Features for Environmental Sound Classification Fusion of GTSC & TEO-GTSC with CNN 81.95% agrawal2017
Learning from Between-class Examples for Deep Sound Recognition EnvNet-v2 (tokozume2017a) + Between-Class learning 81.80% tokozume2017b
🎧 Human accuracy Crowdsourcing experiment in classifying ESC-50 by human listeners 81.30% piczak2015a 📜
Objects that Sound Look, Listen and Learn (L3) network (arandjelovic2017a) with stride 2, larger batches and learning rate schedule 79.80% arandjelovic2017b
Look, Listen and Learn 8-layer convolutional subnetwork pretrained on an audio-visual correspondence task 79.30% arandjelovic2017a
Learning Environmental Sounds with Multi-scale Convolutional Neural Network Multi-scale convolutions with feature fusion (waveform + spectrogram) 79.10% zhu2018
Novel TEO-based Gammatone Features for Environmental Sound Classification GTSC with CNN 79.10% agrawal2017
Learning from Between-class Examples for Deep Sound Recognition EnvNet-v2 (tokozume2017a) + data augmentation 78.80% tokozume2017b
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification CNN with filterbanks learned using convolutional RBM 78.45% sailor2017
Learning from Between-class Examples for Deep Sound Recognition Baseline CNN (piczak2015b) + Batch Normalization + Between-Class learning 76.90% tokozume2017b
Novel TEO-based Gammatone Features for Environmental Sound Classification TEO-GTSC with CNN 74.85% agrawal2017
Learning from Between-class Examples for Deep Sound Recognition EnvNet-v2 (tokozume2017a) 74.40% tokozume2017b
Soundnet: Learning sound representations from unlabeled video 8-layer CNN (raw audio) with transfer learning from unlabeled videos 74.20% aytar2016 📜
Learning from Between-class Examples for Deep Sound Recognition 18-layer CNN on raw waveforms (dai2016) + Between-Class learning 73.30% tokozume2017b
Novel Phase Encoded Mel Filterbank Energies for Environmental Sound Classification CNN working with phase encoded mel filterbank energies (PEFBEs) 73.25% tak2017
Classifying environmental sounds using image recognition networks 16 kHz sampling rate, GoogLeNet on spectrograms (40 ms frame length) 73.20% boddapati2017 📜
Learning from Between-class Examples for Deep Sound Recognition Baseline CNN (piczak2015b) + Batch Normalization 72.40% tokozume2017b
Novel TEO-based Gammatone Features for Environmental Sound Classification Fusion of MFCC & TEO-GTCC with GMM 72.25% agrawal2017
Learning environmental sounds with end-to-end convolutional neural network (EnvNet) Combination of spectrogram and raw waveform CNN 71.00% tokozume2017a
Novel TEO-based Gammatone Features for Environmental Sound Classification TEO-GTCC with GMM 68.85% agrawal2017
Classifying environmental sounds using image recognition networks 16 kHz sampling rate, AlexNet on spectrograms (30 ms frame length) 68.70% boddapati2017 📜
Very Deep Convolutional Neural Networks for Raw Waveforms 18-layer CNN on raw waveforms 68.50% dai2016, tokozume2017b 📜
Classifying environmental sounds using image recognition networks 32 kHz sampling rate, GoogLeNet on spectrograms (30 ms frame length) 67.80% boddapati2017 📜
WSNet: Learning Compact and Efficient Networks with Weight Sampling SoundNet 8-layer CNN architecture with 100x model compression 66.25% jin2017
Soundnet: Learning sound representations from unlabeled video 5-layer CNN (raw audio) with transfer learning from unlabeled videos 66.10% aytar2016 📜
WSNet: Learning Compact and Efficient Networks with Weight Sampling SoundNet 8-layer CNN architecture with 180x model compression 65.80% jin2017
Soundnet: Learning sound representations from unlabeled video 5-layer CNN trained on raw audio of ESC-50 only 65.00% aytar2016 📜
📊 Environmental Sound Classification with Convolutional Neural Networks - CNN baseline CNN with 2 convolutional and 2 fully-connected layers, mel-spectrograms as input, vertical filters in the first layer 64.50% piczak2015b 📜
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks MLP classifier on features extracted with an RNN autoencoder 64.30% freitag2017 📜
Classifying environmental sounds using image recognition networks 32 kHz sampling rate, AlexNet on spectrograms (30 ms frame length) 63.20% boddapati2017 📜
Classifying environmental sounds using image recognition networks CRNN 60.30% boddapati2017 📜
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks 3-layer CNN with vertical filters on wideband mel-STFT (median accuracy) 56.37% huzaifah2017
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks 3-layer CNN with square filters on wideband mel-STFT (median accuracy) 54.00% huzaifah2017
Soundnet: Learning sound representations from unlabeled video 8-layer CNN trained on raw audio of ESC-50 only 51.10% aytar2016 📜
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks 5-layer CNN with square filters on wideband mel-STFT (median accuracy) 50.87% huzaifah2017
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks 5-layer CNN with vertical filters on wideband mel-STFT (median accuracy) 46.25% huzaifah2017
📊 Baseline - random forest Baseline ML approach (MFCC & ZCR + random forest) 44.30% piczak2015a 📜
Soundnet: Learning sound representations from unlabeled video Convolutional autoencoder trained on unlabeled videos 39.90% aytar2016 📜
📊 Baseline - SVM Baseline ML approach (MFCC & ZCR + SVM) 39.60% piczak2015a 📜
📊 Baseline - k-NN Baseline ML approach (MFCC & ZCR + k-NN) 32.20% piczak2015a 📜
A mixture model-based real-time audio sources classification method Dictionary of sound models used for classification (accuracy is computed on segments instead of files) 94.00% baelde2017
NELS - Never-Ending Learner of Sounds Large-scale audio crawling with classifiers trained on AED datasets (including ESC-50) N/A elizalde2017 📜
Utilizing Domain Knowledge in End-to-End Audio Processing End-to-end CNN with learned mel-spectrogram transformation N/A tax2017 📜
Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification Transfer learning from various datasets, including ESC-50 N/A mun2017
Features and Kernels for Audio Event Recognition MFCC, GMM, SVM N/A kumar2016b
A real-time environmental sound recognition system for the Android OS Real-time sound recognition for Android evaluated on ESC-10 N/A pillos2016
Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning Discriminatory effectiveness of different signal representations compared on ESC-10 and Freiburg-106 N/A hertel2016
Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data Combination of weakly labeled data (YouTube) with strong labeling (ESC-10) for Acoustic Event Detection N/A kumar2016a

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:

    filename fold target category esc10 src_file take

    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

Download paper in PDF format

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).
• 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).

You can’t perform that action at this time.