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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

Download Data

Downloading annotations

The dataset is now publicly available for download from here

Paper and Citation

When using these annotations, cite our paper (Accepted at ICASSP 2023 - preprint available on ArXiv):

           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}
Also cite the EPIC-KITCHENS-100 paper where the videos originate:
           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       = {}


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).

Copyright Creative Commons License

The EPIC-SOUNDS dataset is copyright by us and published under the Creative Commons Attribution-NonCommercial 4.0 International 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.

For commercial licenses of EPIC-KITCHENS, email us at

The Team

EPIC-SOUNDS is the result of a collaboration of the Universities of Oxford and Bristol

Jaesung Huh*

University of Oxford

Jacob Chalk*

University of Bristol

Evangelos Kazakos

University of Bristol (now at SAIC)

Dima Damen

University of Bristol

Andrew Zisserman

University of Oxford

Research Funding

The work on EPIC-SOUNDS was supported by:

  • UKRI Engineering and Physical Sciences Research Council (EPSRC) Program Grant Visual AI (EP/T028572/1)
  • UKRI Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Program (DTP)