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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="">
<meta name="author" content="">
<title>EPIC-SOUNDS Dataset</title>
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<h2 class="section-heading text-uppercase">Watch the Trailer</h2>
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<div class="col text-center">
<iframe width="840" height="473" src="https://www.youtube.com/embed/w-Bxat3Cgpk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
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</section>
<section class="bg-light" id="about">
<div class="container">
<div class="row">
<div class="col" align='center'>
<h2 class="service-heading">EPIC-SOUNDS Dataset</h2>
<img src="images/epic-sounds.jpg" width=100%/>
<p>
We introduce EPIC-SOUNDS, a large scale dataset of audio annotations
capturing temporal extents and class labels within the audio stream of the
egocentric videos from EPIC-KITCHENS-100. We propose an annotation pipeline where annotators
temporally label distinguishable audio segments and describe the action that could
have caused this sound. We identify actions that can be discriminated purely from audio,
through grouping these free-form descriptions of audio into classes. For actions that
involve objects colliding, we collect human annotations of the materials of these objects (e.g.
a glass object colliding with a wooden surface), which we verify
from visual labels discarding ambiguities. Overall, EPIC-SOUNDS
includes 75.9k segments of audible events and actions, distributed
across 44 classes. We train and evaluate two state-of-the-art audio
recognition models on our dataset, highlighting the importance of
audio-only labels and the limitations of current models to recognise
actions that sound
</p>
</div>
</div>
</div>
</section>
<section id="downloads">
<div class="container">
<div class="row">
<div class="col-md-12">
<h2 class="section-heading text-uppercase">Download Data</h2>
<img src="images/class_dist.png" width="100%"/>
<h4 class="section-subheading">Downloading annotations</h4>
<p style='font-size:150%'>The dataset is now publicly <a href="https://github.com/epic-kitchens/epic-sounds-annotations">available for download from here</a></p>
<h4 class="section-subheading">Paper and Citation</h4>
<p>When using these annotations, cite our paper (Accepted at ICASSP 2023 - <a href="https://arxiv.org/abs/2302.00646">preprint available on ArXiv</a>):</p>
<pre class="bibtex"><code>@inproceedings{EPICSOUNDS2023,
title={{EPIC-SOUNDS}: {A} {L}arge-{S}cale {D}ataset of {A}ctions that {S}ound},
author={Huh, Jaesung and Chalk, Jacob and Kazakos, Evangelos and Damen, Dima and Zisserman, Andrew},
booktitle = {IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP)},
year = {2023}
} </code></pre>
Also cite the <a href="https://link.springer.com/content/pdf/10.1007/s11263-021-01531-2.pdf">EPIC-KITCHENS-100</a> paper where the videos originate:
<pre class="bibtex"><code>@ARTICLE{Damen2022RESCALING,
title={Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and and Furnari, Antonino
and Ma, Jian and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
journal = {International Journal of Computer Vision (IJCV)},
year = {2022},
volume = {130},
pages = {33–55},
Url = {https://doi.org/10.1007/s11263-021-01531-2}
} </code></pre>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading">Disclaimer </h4>
<p>The underlying data that power EPIC-SOUNDS, EPIC-KITCHENS-100, were collected as a tool for research in computer vision. The dataset may have unintended biases (including those of a societal, gender or racial nature).</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading">Copyright <img alt="Creative Commons License" style="border-width:1px;float:left;margin-right:15px;margin-bottom:0px;" src="https://i.creativecommons.org/l/by-nc/3.0/88x31.png"/></h4>
<p>
The EPIC-SOUNDS dataset is copyright by us and published under the <a rel="license" href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International</a> License. This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
</p>
<p>For commercial licenses of EPIC-KITCHENS, email us at <a href="mailto:[email protected]">[email protected]</a></p>
</div>
</div>
</div>
</section>
<section id="team" class="bg-light">
<div class="container">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">The Team</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<p>EPIC-SOUNDS is the result of a collaboration of the Universities of <a href="https://www.ox.ac.uk">Oxford</a> and <a href='http://www.bristol.ac.uk/'>Bristol</a></p>
</div>
</div>
<div class="row justify-content-center">
<div class="col-md-2">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/jh.jpg" />
<h4>Jaesung Huh*</h4>
<h6 class="text-muted">University of Oxford</h6>
</div>
</div> <!--Jaesung-->
<div class="col-md-2">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/jc.jpg" />
<h4>Jacob Chalk*</h4>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Jacob-->
<div class="col-md-2">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/vk.jpg"/>
<h4>Evangelos Kazakos</h4>
<h6 class="text-muted">University of Bristol (now at CTU Prague)</h6>
</div>
</div> <!--Vangelis-->
<div class="col-md-2">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/dd.jpg" />
<h4>Dima Damen</h4>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Dima-->
<div class="col-md-2">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/az.jpg" />
<h4>Andrew Zisserman</h4>
<h6 class="text-muted">University of Oxford</h6>
</div>
</div> <!--David-->
</div>
<div class="container">
<div class="row">
<div class="col-lg-12">
<h2 class="section-heading text-uppercase">Research Funding</h2>
<div class="text-muted">
<p> The work on EPIC-SOUNDS was supported by:</p>
<ul class="text-muted">
<li>UKRI Engineering and Physical Sciences Research Council (EPSRC) Program Grant Visual AI (EP/T028572/1)</li>
<li>UKRI Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Program (DTP)</li>
</ul>
</div>
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</section>
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<span class="copyright" style="color:#eee;">Copyright © EPIC KITCHENS 2023</span>
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