pith:EFDHBLNA
Towards Open World Sound Event Detection
Sound event detection can now identify unknown events and learn from them incrementally.
arxiv:2605.03934 v2 · 2026-05-05 · cs.SD · cs.AI
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\usepackage{pith}
\pithnumber{EFDHBLNAQUPZNETU7SX4UATXBM}
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Record completeness
Claims
Experimental results demonstrate that our method achieves marginally superior performance compared to existing leading techniques in closed-world settings and significantly improves over existing baselines in open-world scenarios.
That the 1D Deformable architecture combined with feature disentanglement, one-to-many matching, and diversity loss can effectively handle overlapping and ambiguous events while enabling incremental learning of novel sounds.
Introduces OW-SED paradigm and WOOT transformer framework to detect known sounds, identify unseen events, and incrementally learn in open audio environments.
Receipt and verification
| First computed | 2026-05-22T02:04:41.626066Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
214670ada0851f969274fcafca02770b3ad4685be41185b9bcceff7ed5e614ea
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EFDHBLNAQUPZNETU7SX4UATXBM \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 214670ada0851f969274fcafca02770b3ad4685be41185b9bcceff7ed5e614ea
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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