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EPIC-SOUNDS

Introduced by Jaesung Huh et al. in Epic-Sounds: A Large-scale Dataset of Actions That Sound1 Feb 2023 archive 2025-07-28

EPIC-SOUNDS is 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.

Source: Epic-Sounds: A Large-scale Dataset of Actions That Sound

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Audio Classification EPIC-SOUNDS Mirasol3B Accuracy 78.2 Mirasol3B: A Multimodal Autoregressive model for... — 3 Compare
Human Interaction Recognition EPIC-SOUNDS Slow-Fast(Finetune by Fivewin team) Top-1 accuracy % 55.11 Slow-Fast Auditory Streams For Audio Recognition ekazakos/auditory-slow-fast +1 1 Compare

Papers archive 2025-07-28

3 shown of 3 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 12. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

Creative Commons Attribution-NonCommercial 4.0 International

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • EPIC-SOUNDS

1 variant name, as the archive lists them.

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