EPIC-SOUNDS Dataset

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. EPIC-SOUNDS includes 78.4k categorised and 39.2k non-categorised segments of audible events and actions, distributed across 44 classes. In this repository, we provide labelled temporal timestamps for the train / val split, and just the timestamps for the recognition test split. We also provided the temporal timestamps for annotations that could not be clustered into one of our 44 classes, along with the free-form description used during the initial annotation. We train and evaluate two state-of-the-art audio recognition models on our dataset, which we also provide the code and pretrained models for.

Download the Data

A download script is provided for the videos here. You will have to extract the untrimmed audios from these videos. Instructions on how to extract and format the audio into a HDF5 dataset can be found on the Auditory SlowFast GitHub repo. Alternatively, you can email uob-epic-kitchens@bristol.ac.uk for access to an existing HDF5 file.

Contact: uob-epic-kitchens@bristol.ac.uk

Citing

When using the dataset, kindly reference our ICASSP 2023 Paper:

@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}
} 

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

@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}
} 

File Structure

EPIC_Sounds_train.csv

This CSV file contains the annotations for the training set and contains 10 columns:

Column Name	Type	Example	Description
annotation_id	string	P01_01_0	Unique ID for the annotation as a string with participant ID and video ID.
participant_id	string	P01	ID of the participant (unique per participant).
video_id	string	P01_01	ID of the video where the segment originated from (unique per video).
start_timestamp	string	00:00:02.466	Start time in HH:mm:ss.SSS of the audio annotation.
stop_timestamp	string	00:00:05.315	End time in HH:mm:ss.SSS of the audio annotation.
start_sample	int	59184	Index of the start audio sample (24KHz) in the untrimmed audio of video_id
stop_sample	int	127560	Index of the stop audio sample (24KHz) in the untrimmed audio of video_id
description	string	paper rustle	Transcribed English description provided by the annotator.
class	string	rustle	Parsed class from the description.
class_id	int	3	Numeric ID of the class.

EPIC_Sounds_validation.csv

This CSV file contains the annotations for the validation set and contains 10 columns:

Column Name	Type	Example	Description
annotation_id	string	P01_01_0	Unique ID for the annotation as a string with participant ID and video ID.
participant_id	string	P01	ID of the participant (unique per participant).
video_id	string	P01_01	ID of the video where the segment originated from (unique per video).
start_timestamp	string	00:00:02.466	Start time in HH:mm:ss.SSS of the audio annotation.
stop_timestamp	string	00:00:05.315	End time in HH:mm:ss.SSS of the audio annotation.
start_sample	int	59184	Index of the start audio sample (24KHz) in the untrimmed audio of video_id
stop_sample	int	127560	Index of the stop audio sample (24KHz) in the untrimmed audio of video_id
description	string	paper rustle	Transcribed English description provided by the annotator.
class	string	rustle	Parsed class from the description.
class_id	int	3	Numeric ID of the class.

EPIC_Sounds_recognition_test_timestamps.csv

This CSV file contains the annotations for the recognition testing set and contains 7 columns:

Column Name	Type	Example	Description
annotation_id	string	P01_01_0	Unique ID for the annotation as a string with participant ID and video ID.
participant_id	string	P01	ID of the participant (unique per participant).
video_id	string	P01_01	ID of the video where the segment originated from (unique per video).
start_timestamp	string	00:00:02.466	Start time in HH:mm:ss.SSS of the audio annotation.
stop_timestamp	string	00:00:05.315	End time in HH:mm:ss.SSS of the audio annotation.
start_sample	int	59184	Index of the start audio sample (24KHz) in the untrimmed audio of video_id
stop_sample	int	127560	Index of the stop audio sample (24KHz) in the untrimmed audio of video_id

sound_events_not_categorised.csv

This CSV file contains the annotations that could not be clustered into our 44 classes and contains 8 columns:

Column Name	Type	Example	Description
annotation_id	string	P01_01_NC_0	Unique ID for the annotation as a string with participant ID and video ID.
participant_id	string	P01	ID of the participant (unique per participant).
video_id	string	P01_01	ID of the video where the segment originated from (unique per video).
start_timestamp	string	00:00:02.466	Start time in HH:mm:ss.SSS of the audio annotation.
stop_timestamp	string	00:00:05.315	End time in HH:mm:ss.SSS of the audio annotation.
start_sample	int	59184	Index of the start audio sample (24KHz) in the untrimmed audio of video_id
stop_sample	int	127560	Index of the stop audio sample (24KHz) in the untrimmed audio of video_id
description	string	paper rustle	Transcribed English description provided by the annotator.

License

All files in this dataset are copyright by us and published under the Creative Commons Attribution-NonCommerial 4.0 International License, found here. 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.

Disclaimer

EPIC-KITCHENS-55 and EPIC-KITCHENS-100 were collected as a tool for research in computer vision, however, it is worth noting that the dataset may have unintended biases (including those of a societal, gender or racial nature).