10.7910/DVN/YDEPUTKarol J. PiczakKarolJ. Piczak0000-0002-6115-0833Warsaw University of TechnologyHarvard Dataverse2015Computer and Information Scienceenvironmental soundclassificationdatasetKarol J. PiczakKarolJ. PiczakWarsaw University of Technology2015-04-162015-10-184724118924512260722199941125882634114271805311368348671147758000114307199711131698521110860210112750688411406826751152263374112510491211529444071141572080114115697011418410101157872865114545171511655375211149383565113790149311485157331146274692115373269211656654321153893991application/zipapplication/zipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzipapplication/x-gzip2.0<p>The <strong>ESC dataset</strong> is a collection of short environmental recordings available in a unified format (5-second-long clips, 44.1 kHz, single channel, Ogg Vorbis compressed @ 192 kbit/s). All clips have been extracted from public field recordings available through the <a href="http://freesound.org">Freesound.org project</a>. Please see the README files for a detailed attribution list. The dataset is available under the terms of the <a href="http://creativecommons.org/licenses/by-nc/3.0/">Creative Commons license - Attribution-NonCommercial</a>.</p> <p>The dataset consists of three parts:</p> <ul> <li><strong><a href="https://github.com/karoldvl/ESC-50">ESC-50</a></strong>: a labeled set of 2 000 environmental recordings (50 classes, 40 clips per class),</li> <li><strong><a href="https://github.com/karoldvl/ESC-10">ESC-10</a></strong>: a labeled set of 400 environmental recordings (10 classes, 40 clips per class) (this is a subset of ESC-50 - created initialy as a proof-of-concept/standardized selection of easy recordings),</li> <li><strong>ESC-US</strong>: an unlabeled dataset of 250 000 environmental recordings (5-second-long clips), suitable for unsupervised pre-training.</li> </ul> <p>The ESC-US dataset, although not hand-annotated, includes the labels (tags) submitted by the original uploading users, which could be potentially used for weakly-supervised learning (noisy and/or missing labels). The ESC-10 and ESC-50 datasets have been prearranged into 5 uniformly sized folds so that clips extracted from the same original source recording are always contained in a single fold.</p> <p>The labeled datasets are also available as GitHub projects: <a href="https://github.com/karoldvl/ESC-50">ESC-50</a> | <a href="https://github.com/karoldvl/ESC-10">ESC-10</a>.</p> <p>For a more thorough description and analysis, please see the <a href="http://karol.piczak.com/papers/Piczak2015-ESC-Dataset.pdf">original paper</a> and the <a href="https://github.com/karoldvl/paper-2015-esc">supplementary IPython notebook</a>.</p> <hr> <p>The goal of this project is to facilitate open research initiatives in the field of environmental sound classification as publicly available datasets in this domain are still quite scarce.</p> <p><strong>Acknowledgments</strong><br/> I would like to thank <a href="http://www.dtic.upf.edu/~ffont/">Frederic Font Corbera</a> for his help in using the Freesound API.</p>Notes