1. Epic-Sounds: A Large-scale Dataset of Actions That Sound
- Author
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Huh, J, Chalk, J, Kazakos, E, Damen, D, and Zisserman, A
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Sound (cs.SD) ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Audio and Speech Processing (eess.AS) ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing ,Machine Learning (cs.LG) - Abstract
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. 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 being placed on a wooden surface), which we verify from visual labels, discarding ambiguities. Overall, EPIC-SOUNDS includes 78.4k categorised segments of audible events and actions, distributed across 44 classes as well as 39.2k non-categorised segments. 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., Comment: 6 pages, 4 figures
- Published
- 2023
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